Plant Macronutrient Use Efficiency: Molecular and Genomic Perspectives in Crop Plants 9780128113080

Table of contents :
Content: 1. Molecular and Genetic Basis of Plant Macronutrient Use Efficiency: Concepts, Opportunities, and Challenges 2. Role of Nutrient-Efficient Plants for Improving Crop Yields: Bridging Plant Ecology, Physiology, and Molecular Biology 3. Macronutrient Sensing and Signaling in Plants 4. The Significance of Nutrient Interactions for Crop Yield and Nutrient Use Efficiency 5. The Contribution of Root Systems to Plant Nutrient Acquisition 6. Molecular Genetics to Discover and Improve Nitrogen Use Efficiency in Crop Plants 7. The Role of Root Morphology and Architecture in Phosphorus Acquisition: Physiological, Genetic, and Molecular Basis 8. Potassium Sensing, Signaling, and Transport: Toward Improved Potassium Use Efficiency in Plants 9. Understanding Calcium Transport and Signaling, and its use Efficiency in Vascular Plants 10. The Role of Calcium in Plant Signal Transduction Under Macronutrient Deficiency Stress 11. Magnesium Homeostasis Mechanisms and Magnesium use Efficiency in Plants 12. Advances in Understanding Sulfur Utilization Efficiency in Plants 13. Water Availability and Nitrogen use in Plants: Effects, Interaction, and Underlying Molecular Mechanisms 14. NPK Deficiency Modulates Oxidative Stress in Plants 15. Genetic Improvements of Traits for Enhancing NPK Acquisition and Utilization Efficiency in Plants 16. Endophytic Bacteria and Rare Earth Elements
Promising Candidates for Nutrient use Efficiency in Plants 17. Introduction to GWAS and MutMap for Identification of Genes/QTL using Next-Generation Sequencing 18. Transgenic Approaches for Improving Phosphorus use Efficiency in Plants 19. Transgenic Approaches for Improving Nitrogen and Potassium use Efficiency in Plants 20. Future Climate Change and Plant Macronutrient use Efficiency

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Plant Macronutrient Use Efficiency Molecular and Genomic Perspectives in Crop Plants

Edited by

Mohammad Anwar Hossain Takehiro Kamiya David J. Burritt Lam-Son Phan Tran Toru Fujiwara

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1800, San Diego, CA 92101-4495, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2017 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-811308-0 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: André Gerhard Wolff Acquisition Editors: Nancy Maragioglio and Mary Preap Editorial Project Manager: Billie Jean Fernandez Production Project Manager: Punithavathy Govindaradjane Designer: Victoria Pearson Typeset by Thomson Digital

List of Contributors Miguel J. Beltran-García Autonomous University of Guadalajara, Zapopan, Jalisco, Mexico Philip N. Benfey Howard Hughes Medical Institute, Duke University, Durham, NC, United States Hermi F. Brito Institute of Chemistry, University of São Paulo, São Paulo, Brazil Sylvie M. Brouder Purdue University, West Lafayette, IN, United States Juan J. Camacho-Cristóbal University Pablo de Olavide, Sevilla, Spain Carlos J. Ceacero University Pablo de Olavide, Sevilla, Spain Maria C.F. Cunha Felinto Nuclear and Energy Research Institute (IPEN-CQMA), São Paulo, Brazil Ivan G.N. da Silva Institute of Chemistry, University of São Paulo, São Paulo, Brazil Sylvia M. de Sousa Embrapa Maize and Sorghum, Sete Lagoas, Minas Gerais, Brazil Paolo Di Mascio Institute of Chemistry, University of São Paulo, São Paulo, Brazil Setyowati R. Djiwanti Indonesian Spice and Medicinal Crop Research Institute (ISMECRI), Bogor, Indonesia Francisco Echaide-Aquino Autonomous University of Guadalajara, Zapopan, Jalisco, Mexico Toru Fujiwara University of Tokyo, Tokyo, Japan Trevor Garnett University of Adelaide, Adelaide, SA, Australia Agustín González-Fontes University Pablo de Olavide, Sevilla, Spain Claudia T. Guimaraes Embrapa Maize and Sorghum, Sete Lagoas, Minas Gerais, Brazil

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Wanli Guo Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China Xue He Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China María B. Herrera-Rodríguez University Pablo de Olavide, Sevilla, Spain Aurora Huerta-Robles Autonomous University of Baja California, Mexicali, Baja California, Mexico Sameer Joshi Agriculture Victoria, Grains Innovation Park, Horsham, VIC, Australia Surya Kant Agriculture Victoria, Grains Innovation Park, Horsham, VIC, Australia Suresh Kaushik Indian Agricultural Research Institute, New Delhi, India Julia Kehr University of Hamburg, Hamburg, Germany Leon V. Kochian Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada Thomas Leustek Rutgers University, New Brunswick, NJ, United States Damar L. López-Arredondo StelaGenomics, Guanajuato, México Per-Olof Lundquist Uppsala BioCenter, and Linnean Centre for Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden Jonathan P. Lynch Pennsylvania State University, University Park, PA, United States Gloria Macedo-Raygoza Autonomous University of Baja California, Mexicali, Baja California, Mexico Jurandir V. Magalhaes Embrapa Maize and Sorghum, Sete Lagoas, Minas Gerais, Brazil Hayato Maruyama Hokkaido University, Sapporo, Japan Marisa H.G. Medeiros Institute of Chemistry, University of São Paulo, São Paulo, Brazil María T. Navarro-Gochicoa University Pablo de Olavide, Sevilla, Spain

List of Contributors

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Giao N. Nguyen Agriculture Victoria, Grains Innovation Park, Horsham, VIC, Australia Yoshihiro Ohmori University of Tokyo, Tokyo, Japan Mamoru Okamoto University of Adelaide, Adelaide, SA, Australia Darren Plett University of Adelaide, Adelaide, SA, Australia Fernanda M. Prado Institute of Chemistry, University of São Paulo, São Paulo, Brazil Katia R. Prieto Institute of Chemistry, University of São Paulo, São Paulo, Brazil Martin Reich Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands; Secretariat of the German Bioeconomy Council, Berlin, Germany Jesús Rexach University Pablo de Olavide, Sevilla, Spain Lenin Sánchez-Calderón StelaGenomics, Guanajuato, México Deepti Shankhdhar G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Shailesh C. Shankhdhar G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Ashish Sharma DAV University, Jalandhar, Punjab, India Ryoung Shin RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan Erin E. Sparks Howard Hughes Medical Institute, Duke University, Durham, NC, United States Wan Teng Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Yiping Tong Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Hellen P. Valério Institute of Chemistry, University of São Paulo, São Paulo, Brazil Jeffrey J. Volenec Purdue University, West Lafayette, IN, United States

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Qinglian Wang Henan Collaborative Innovation Center of Modern Biological Breeding, Henan Institute of Science and Technology, Xinxiang, China Jun Wasaki Hiroshima University, Higashi-Hiroshima, Japan Martin Weih Crop Production Ecology, and Linnean Center for Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden Christian Weissert University of Hamburg, Hamburg, Germany Anna Westerbergh Uppsala BioCenter, and Linnean Centre for Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden James F. White, Jr Rutgers University, New Brunswick, NJ, United States Kenji Yano University of Tokyo, Tokyo, Japan Lenin Yong-Villalobos Unidad de Genómica Avanzada del Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Guanajuato, México Baohong Zhang East Carolina University, Greenville, NC, United States Zhiyong Zhang Henan Collaborative Innovation Center of Modern Biological Breeding, Henan Institute of Science and Technology, Xinxiang, China Zhi-Liang Zheng City University of New York, Bronx, NY, United States

Editors’ Biographies Dr. Mohammad Anwar Hossain is a Professor in the Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh, Bangladesh. He received his BSc in Agriculture and MS in Genetics and Plant Breeding from Bangladesh Agricultural University, Bangladesh. He also received an MSc in Agriculture from Kagawa University, Japan in 2008 and a PhD in Abiotic Stress Physiology and Molecular Biology from Ehime University, Japan in 2011. In November 2015, he moved to Tokyo University, Japan, as a JSPS postdoctoral researcher to work on isolating low-phosphorus stress tolerant genes/QTLs from rice. He has over 50 peer-reviewed publications on important aspects of plant physiology and breeding, plant nutrition, plant stress responses and tolerance mechanisms, and exogenous chemical priminginduced abiotic stress tolerance. He has edited four book volumes, including this one, published by Elsevier, CRC press and Springer. He is a professional member of International Metabolomics Society, Bangladesh Society of Genetics and Plant Breeding, Bangladesh Association for Plant Tissue Culture and Biotechnology, and the Seed Science Society of Bangladesh. Dr. Takehiro Kamiya is an Associate Professor at the Laboratory of Plant Nutrition and Fertilizers, Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, University of Tokyo, Japan. He obtained his PhD in 2006 from Nagoya University, Japan. After doing his postdoctoral research at the Nagoya University (2006–07), University of Tokyo (2007–10), Aberdeen University (2010–12), he accepted the position of lecturer at the University of Tokyo. Since 2015, he has been an Associate Professor at the University of Tokyo. His current research interests are understanding of the essential and nonessential element dynamics in plants using ICP-MS and hyperspectral camera. He is also interested in molecular mechanisms of Casparian strip formation. Dr. David J. Burritt is an Associate Professor in the Department of Botany, The University of Otago, Dunedin, New Zealand. He received his BSc and MSc (Hons) in Botany, and his PhD in Plant Biotechnology from The University of Canterbury, Christchurch, New Zealand. His research interests include oxidative stress and redox biology, plant-based foods and bioactive molecules, plant breeding and biotechnology, cryopreservation of germplasm, and the stress biology of plants, animals, and algae. He has over 100 peer-reviewed publications and in addition to this volume has edited two books for Springer.

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Dr. Lam-Son Phan Tran is Head of the Signaling Pathway Research Unit at RIKEN Center for Sustainable Resource Science, Japan. He obtained his MSc in Biotechnology in 1994 and PhD in Biological Sciences in 1997, from Szent Istvan University, Hungary. After doing his postdoctoral research at the National Food Research Institute (1999–2000) and the Nara Institute of Science and Technology of Japan (2001), in October 2001, he joined the Japan International Research Center for Agricultural Sciences to work on the functional analyses of transcription factors and osmosensors in Arabidopsis plants under environmental stresses. In August 2007, he moved to the University of Missouri, Columbia, USA as a Senior Research Scientist to coordinate a research team working to discover soybean genes to be used for genetic engineering of drought-tolerant soybean plants. His current research interests are elucidation of the roles of phytohormones and their interactions in abiotic stress responses, as well as translational genomics of legume crops with the aim to enhance crop productivity under adverse environmental conditions. He has published over 110 peer-reviewed papers with more than 80 research and 30 review articles, contributed 8 book chapters to various book editions published by Springer, Wiley-Blackwell, and American Society of Agronomy, Crop Science Society of America and Soil Science Society of America. In addition to this current book, he has also edited seven books for Springer. Dr. Toru Fujiwara is a Professor at the Laboratory of Plant Nutrition and Fertilizers, Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, University of Tokyo, Japan. He obtained his PhD in 1992 from University of Tokyo, ­Japan. He worked in several institutions in his early carrier, including Washington University in St. Louis, University of California, Davis, and Cornell University. Since 2011, he is at the current position. He has worked and is presently continuing on a wide range of topics, including plant nutrient transport, long-distance transport of nutrients and macromolecules, regulation of nutrient transport processes, mathematical modeling of nutrient transport, and generation of low nutrient tolerant plants.

Preface It has been projected that there will be 9.2 billion people on the planet by 2050, and recent estimates suggest that food production will have to increase by 70% to meet the demand for food. Sustainable crop production, delivering high yields to meet ever-increasing demands, is a major challenge for the scientific community and will require the combined expertise of nutritionists, agronomists, farmers, plant breeders, and scientists. Nutrients can be classified into two distinct groups, depending on their concentrations in plant tissues, namely macro- and micronutrients whose specific roles in plant metabolism are as diverse as their physicochemical properties. Recently, plant nutrient use efficiency (NUE), which involves investigation and optimization of nutrient use by plant in response to the increasing fragility of our natural resources and threats to food security across the globe, has attracted considerable interest from the scientific community. Countless studies in various scientific disciplines and dealing with different plant species under different conditions have now focused on the NUE of plants. While significant progress has been made over the last few years improving crops with respect to this complex trait, several issues remain to be solved and understanding the physiological, molecular, and genetic mechanisms controlling NUE, as well as nutrient stress perception, transduction, and tolerance, is still a challenge for plant biologists. Further improvement of macro-NUE by plants is important if we are to reduce production costs, expand crop cultivation to noncompetitive marginal lands with low-nutrient resources, and prevent environmental damage. The discovery of novel genes, the analyses of their expression patterns in response to low/high-nutrient stress and understanding their functions is critical for increasing plant production and stress adaptation. Uncovering the molecular mechanisms controlling NUE and crop responses to nutrient stresses will provide a basis for effective strategies to enhance crop tolerance to stress and sustainable utilization of natural resources. The chapters in this book, written by recognized experts in the field, aim to provide an up to date overview of the molecular and genetic bases of macronutrient use efficiency in plants and highlight strategies, where NUE will lead to enhanced crop productivity under low- or excessive-nutrient conditions and help to control the nutrient dynamics in the soil–plant–atmosphere continuum. This volume will serve as a key source of information and knowledge to graduate and postgraduate students, teachers, and plant scientists around the globe. We believe that it will be of interest to a wide range of plant and soil scientists, including plant breeders, biotechnologists, molecular biologists, agronomists, and physiologists, who are interested in maximizing nutrient uptake and assimilation, and boosting crop productivity in low-nutrient soil or with reduced fertilizer utilization, and thus help to avoid degradation of the environment. We believe that the information covered in this book will make a sound contribution to this fascinating area of research. Mohammad Anwar Hossain, Professor, Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh, Bangladesh Takehiro Kamiya, Associate Professor, Laboratory of Plant Nutrient and Fertilizers, University of Tokyo, Japan

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Preface

David J. Burritt, Associate Professor, Department of Botany, University of Otago, Dunedin, New Zealand Lam-Son Phan Tran, Unit Leader, Signaling Pathway Research Unit, RIKEN Center for Sustainable Resource Science, Yokohama, Japan Toru Fujiwara, Professor, Laboratory of Plant Nutrient and Fertilizers, University of Tokyo, Japan

Acknowledgments The editors express their heart-felt gratitude to all of the contributors to this volume, who were eager to share their knowledge and experiences of macronutrient use efficiency in crop plants. We would like to extend thanks to Mary Preap, the Elsevier associate acquisition editor, and Billie Jean Fernandez, the Elsevier editorial project manager, who enabled this book project. Finally, our special thanks to all of the staff members of Elsevier, who where directly or indirectly involved in preparation of this book, for their steady support and efforts to ensure timely publication of this volume.

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MOLECULAR AND GENETIC BASIS OF PLANT MACRONUTRIENT USE EFFICIENCY: CONCEPTS, OPPORTUNITIES, AND CHALLENGES

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Damar L. López-Arredondo*, Lenin Sánchez-Calderón*, Lenin Yong-Villalobos** *StelaGenomics, Guanajuato, México **Unidad de Genómica Avanzada del Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Guanajuato, México

INTRODUCTION A balanced nutrition is required at each stage of plant development to achieve maximum yield. Mineral elements such as phosphorus (P), nitrogen (N), calcium (Ca), and potassium (K), among others, perform essential roles in all living organisms, therefore, must be continually available for plant uptake. However, in most soils, one or more of these nutrients can limit plant growth due to different factors, including low diffusion rates, rhizosphere microbial activity and/or soil physicochemical properties. Among all the nutrients required by plants, the macronutrients P, N, and K are the most limiting factors for crop yield in most soils, making necessary the yearly application of high amounts of fertilizers. The “Green Revolution” boosted the continue development of new and improved modern varieties and greater use of mineral fertilizers and pesticides, which directly impacted food production. For instance, maize global production experienced a significant increase from 205 million tons in 1961 to 1037 million tons in 2014, being the Americas major producer with the 54%.1 However, in Africa where water scarcity, food availability, and malnutrition are serious problems, maize production was only 7.2% of the total in the same period, in which the Sub-Sahara region had the lowest yields.1 Poor efficiency of fertilizers usage, prevalence of marginal soils and limited financial resources and infrastructure to access agricultural inputs in many countries are major constraints contributing to low crop yields. Furthermore, traditional plant breeding programs appear insufficient to ensure future global food demand. Therefore, modern breeding programs based on the use of genomic information together with transgenic and genome editing approaches are needed to increase food production while addressing environmental issues and optimizing fertilizers and pesticides use. Knowledge on the genetic basis of plant nutrition is increasing rapidly and is making possible the identification and management of key regulatory elements involved in nutrient acquisition, transport and assimilation, as well as in root system architecture (RSA) and function. In this chapter, we first review the importance of the major macronutrients for supporting food production, then, we provide some basic concepts regarding of macronutrient acquisition and use efficiencies, and review current knowledge on key genes regulating acquisition and assimilation in model plants and crops. Finally, we Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00001-6 Copyright © 2017 Elsevier Inc. All rights reserved.

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present relevant results from manipulation of some of these genes, which potentially can be used to improve nutrient use efficiency (NUE) in several crops, as well as emerging challenges and opportunities to improve related traits to contribute with the increasing global food demand.

WHY MACRONUTRIENTS ARE IMPORTANT FOR PLANTS? Plant growth and reproduction are the result of complex processes whereby plants efficiently use solar energy, carbon dioxide, water, and nutrients from the soil. These environmental factors are highly variable in natural and agricultural ecosystems. However, the availability of mineral nutrients is a major constraint for plant growth and development in most ecosystems, as they must be present in a usable form, in sufficient amount, and in appropriate ratios.2 Plants take up relatively large amounts of macronutrients: P, N, K, Ca, sulphur (S), and magnesium (Mg), which normally represent between 0.2% and 4% of the total plant dry weight, whereas micronutrients (boron, chlorine, iron, nickel, zinc, copper, manganese, and molybdenum) are required in small quantities by plants and represent less than 0.01% of their dry weight. Among macronutrients, P, N and K are often the major limiting factors for optimal plant development as they are required for essential processes and a suboptimal supply severely limits crop yield (Fig. 1.1). P and N are crucial components of DNA and RNA. Phosphate esters form part of phospholipids and intermediates in many metabolic pathways of biosynthesis and degradation. P also plays a vital role controlling cell signaling pathways through phosphorylation of proteins and lipids during plant responses to developmental and environmental cues3 (Fig. 1.1). N, comprising 16% of total plant protein and 1.5% to 2% of plant dry matter, is a component of all amino acids and proteins, is an integral part of chlorophyll and plays a central role in cellular metabolism (Fig. 1.1).4 Different from these two major elements, K does not form a part of metabolic molecules in the plant. However, its levels range from 1% to 3% dry weight in plant tissue and plays a substantial role as a provider of electrical charges in the cells and as catalysts of numerous central enzymatic processes. Additionally, K plays a critical role in osmoregulation of water use (opening and closing of stomata) and is involved in plant tolerance to stresses, such as high/low temperatures, drought, diseases, and pests5 (Fig. 1.1). Given the importance of P, N, and K in vital biological processes and development, negative effects of deficient or suboptimal levels in plant nutrition have been extensively recognized. Poor P nutrition leads to yield losses in the range of 10%–15% of maximal yields; in cereals, low-P supplementation reduces tillering, whereas in legumes branching is strongly reduced. Crop quality is also negatively affected by P-, N-, and K-defficiencies in forages, fruits and vegetables. Seed and fruit formation is specially affected by low-Pi availability conditions, whereas K deficiency impacts the shelf life of fruits and vegetables, as well as the quality of grain and forage crops. Extensive experimentation with different plant species demonstrated the high rates of nutrient consumption by crops; for instance, P uptake rate per unit length of root is 10 times higher in 20 day-old plants than in 30 day-old plants6; barley (Hordeum vulgare L.), cotton (Gossypium hirsutum L.), and sugar beet (Beta vulgaris) take as much as 6 and 8 kg of K per hectare per day.7,8 Therefore, to sustain this rate of consumption, and avoid imbalances and interferences between nutrient metabolism, available forms of macronutrients must be replenished in the soil solution. However, in Central Europe, agriculture is based in K mining which affects water use efficiency and therefore, crop yields each season.

 Why macronutrients are important for plants?

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FIGURE 1.1  Role of Phosphorus, Nitrogen, and Potassium in Plant Growth and Their Availability in the Soil Phosphorus, nitrogen, and potassium are essential macronutrients for plant growth and yield. The cation K+, phosphate (Pi), and nitrate (NO −3 ), are the forms in which plants preferentially use them and form part of macromolecules, play crucial roles in cellular processes (e.g., enzyme activation, osmoregulation, and stomatal activity), and finally impact general plant growth and yield quality. However, their availability is subjected to several factors, such as pH, organic matter, and cations, among others, in the soil solution. Atmospheric nitrogen (N2), can be fixed by soil bacteria, but the NO −3 present in the soil is also converted in N2 by denitrification. Red and blue dashed arrows denote atmospheric N2 fixation and denitrification by bacteria, respectively; whereas green solid arrow denotes the release and fixation of K+. AM, Arbuscular mycorrhizal; NH 4+, ammonium; Ca2+, calcium; Fe, iron; Al3+, aluminium.

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To access P, N, and K under harsh conditions, plants have evolved highly specialized mechanisms and regulatory networks that allow them to increase nutrient availability in the rhizosphere, and enhance their acquisition and internal use, permitting adequate intracellular concentrations (Fig. 1.1) (the interested reader is referred to relevant papers.5,9–16 These strategies allow plants to contend with nutrient scarcity and thus represent the basis for crop improvement.17 As ensuring P, N, and K fertilization for crop plants it is crucial to obtain optimal yields, improving acquisition and internal utilization of these three macronutrients trough the manipulation of the molecular and genetic elements involved in their uptake, mobilization and metabolism is the most promising approach to produce plant varieties with enhanced NUE. Therefore, the following sections will be focused to present relevant information regarding P, N, and K.

THE ROLE OF MACRONUTRIENTS FOR A SUSTAINABLE INTENSIFICATION OF CROPPING SYSTEMS Soil degradation caused by erosion, nutrient depletion, acidification, and many other factors affect agriculture in many regions of the world. In fact, poor fertility predominates in about 50% of the agricultural soils of the world, including 75% of the agricultural soils of Africa and nearly 40% in America.18–20 Therefore, application of mineral fertilizers helps to ensure an effective use of both land and water. Unfortunately, in Africa, for instance, where productivity is strongly affected by Pi-deficiency and the requirements of food are huge, fertilizers application is still negligible.1,19 This situation is triggered by factors of different nature, since the physicochemical properties of nutrients and soils, which determine their availability, the access to financial resources, to the unequal global distribution of nutrient reserves.

AVAILABILITY OF NUTRIENTS IN THE SOIL Soil fertility depends not only on the quantity and quality of nutrients present in the soil, but also on its physical, biological, and chemical properties. Therefore, although total content in the soil might be huge, many nutrients are poorly available for plant uptake because of a series of physical and chemical reactions, and biological processes that influence the form in which they exist in the soil (Fig. 1.1).20 P and N are present as organic and inorganic chemical forms in the soil (Fig. 1.1). Inorganic phosphate (orthophosphate, Pi), is the main chemical form that plants can take up and metabolize, and is present in the soil mainly in the form of apatite and other complexes. Pi concentration in soils ranges between 200 and 800 mg kg−1, however, available Pi in the soil solution is highly variable, being as low as 10−8 M in soils with very low fertility, which predominate in many tropical and subtropical regions of the world, particularly in the areas of acid soils.21 Plants can acquire both organic- and inorganicN forms from the soil [nitrate (NO −3 ), ammonium (NH +4 ), urea, and amino acids], but they are unable to complete their life cycle using exclusively organic-N.22,23 Therefore, in this chapter we will mainly present data regarding NO −3 metabolism. Under good aeration conditions, NO −3 is the dominant form in soils, while NH +4 tend to accumulate in flooded and acid soils. In natural soils, both Pi and N-forms availability is generally low and can be highly variable depending on various factors including soil physical properties, leaching, and microbial activity, which often result in the formation of depleted areas.12 Since relatively high solubility of Pi only occurs in pHs between 6.5 and 7.5 and the great

 sustainable intensification of cropping systems

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majority of soils have alkaline or acidic pHs, Pi is the nutrient that limits growth on most environments because of its low availability and its great demand by plants and microorganisms.24 In the case of N, specialized free-living and symbiotic bacteria fixate atmospheric N (N2), producing NH +4 , which in turn can be incorporated in plant metabolism. It is estimated that this process contributes with the fixation of about 20–22 million tons of N each year4 (Fig. 1.1). A similar scenario is faced by K, which is a more widely distributed and abundant element than P in the soil but is also poorly available for plant uptake due similar factors (Fig. 1.1). K only occurs as inorganic forms in the soils, and is available as a positively charged ion (K+). Generally, the upper soils profile contains between 3,000 and 100,000 kg ha−1 of K, but only a small fraction (1%–2%) is ready available5,25 (Fig. 1.1). K+ depletion is recognized as a major reason of arable land degradation in Southeast Asia, Latin America, and the Caribbean.26 Macronutrients need to be replenished into the soil from external sources once original nutrient availability has been decreased by crop cultivation or lost by erosion processes. These external sources include input from residual organic matter from cultivated crops, commercial fertilizers, wastewater from human activities, and animal manures. Manures have been extensively proposed as an effective alternative to reduce mineral fertilizers consumption in organic farming and is a widespread practice in Africa.20,27 However, this strategy is unfeasible to sustain extensive and intensive agriculture, and grassland also needs to be fertilized to effectively support animal feeding. Application of mineral fertilizers is an absolute necessary practice to improve crop yield in intensive agriculture systems.

USE OF FERTILIZERS AND NUTRIENT RESERVES Given the need to increase food production, fertilizer consumption has steadily increased during the last 50 years. Total world consumption of fertilizers (including P, N, and K nutrient elements) was closed to 200 million tons in 2014, with the major tonnage applied to maize, rice, wheat, and soybean.1 N-fertilizers are practically unlimited because they are produced from N2 present in the atmosphere by the Haber-Bosch process which, although is high-energy demand, has greatly facilitated the success of modern agriculture to achieve higher yields. In contrast, Pi- and K+-fertilizers are mined and refined from ore deposits in the earth’s crust and, therefore are constrained by mineral reserves, which are unevenly distributed globally. For instance, phosphate rock reserves are located mainly in Morocco, USA, and China.28 Current economically exploitable K+ reserves are of over 3,700,000 million tons, whereas phosphate rock reserves, from which Pi-fertilizers are produced, are more limited and nonrenewable.28 This P-situation if further complicated because of the extremely inefficient use of Pi-fertilizers in the field, as only 20%–30% of the applied fertilizer is used by crops and the rest is lost due to fixation in the soil and conversion by microbes into organic forms not amenable for plant uptake. Although efforts searching for new high quality phosphate rock deposits are in progress, estimates for the duration of current rock phosphate reserves vary within a lapse from 50 to 200 years. This scenario is unfeasible because without inexpensive sources of Pi, agriculture would be nonprofitable or food prices would have to double or triple.27,29 Therefore, more efficient fertilization strategies and technologies for the recovery of Pi from human, animal, and agricultural wastes must be also developed. Moreover, serious environmental concerns have raised from the abuse and inefficient use of NO −3 and Pi-fertilizers because in most agricultural systems a considerable amount of them is lost through run-off, leaching, or as volatile nitrous oxide, or NH +4 to the environment. Nitrous oxide emissions contribute to the depletion of the ozone layer, while volatilized NH +4 is returned as wet or dry deposition,

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which can cause global acidification and, together with Pi run-off and leaching, cause eutrophication of terrestrial and aquatic ecosystems.30–32

MACRONUTRIENT USE EFFICIENCY; CONCEPTS AND IMPORTANCE SOME BASIC CONCEPTS It is estimated that the number of humans supported per hectare of arable land has increased from 1.9 to 4.3 persons between 1908 and 2008. With the current increase of global population and the climate change, this value will substantially increase in the following decades. Therefore, optimizing fertilizer use to increase food production per area unit and at the same time reduce the environmental impact of agriculture, is a pivotal aim to contribute to a more sustainable agriculture. It is very clear that facing these challenges in a global sense will require the integration of nutrient management strategies and approaches at a multilevel and multidisciplinary scale. The term of “use efficiency” has received remarkably importance and, in general, it is recognized that use efficiency of the most limiting plant growth macronutrients is a highly complex trait influenced by numerous factors. Interaction of both environmental and plant intrinsic factors determine the efficiency by which plants use nutrients to produce biomass and/or grain. Therefore, coining a biologically significant and operational index, such as NUE has been of great interest for crop improvement and modern agriculture. Through the years, NUE has been widely discussed and many definitions and evaluation processes have been proposed, and more recently the idea of considering a more dynamic concept has raised.33 This is contributing to a better understanding of macro-NUE and, therefore, enabling the evaluation and comparison of this trait among plant species and individuals of specie–specific populations. Basically, NUE is defined in terms of yield and input, and can be described as the relationship between the amount of nutrients acquired by plants and the resulting production of biomass (fruits, forage, and grains). However, it is important that macro-NUE must be understood as a global concept encompassing multiple elements of the cropping system and their interrelationship, which provide information under specific conditions and that are evaluated with a specific purpose. Therefore, numerous terms, formulae, measurements and calculations have been proposed, and reviewed. These measurements are long- or short-term indicators, and provide valuable information regarding the productivity of the cropping system, the effect of fertilizer input on productivity, the proportion of nutrients taken up and lost, and the ability of a plant to transform nutrients into yield in economic terms, particularly when the goal is the evaluation of different genotypes for breeding.34 Moreover, the combination of more than one of these evaluations is recommended to have more data and more precise diagnoses.

COMPONENTS OF NUTRIENT USE EFFICIENCY Macro-NUE is a complex trait comprising physiological and developmental processes (including acquisition, translocation, assimilation, and internal remobilization) in plants exposed to variable environmental conditions. Therefore, macro-NUE is determined by: (1) plant genotype (species and cultivars); (2) environmental conditions (rainfall, temperature, soil texture, pH variation, and nutrient concentration); and (3) technological and agronomic practices (crop density, spatial arrangement of

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plants, rate and methods of fertilization, and irrigation). Additionally, the mathematical model used for the evaluations is also important. For all these reasons, to have a confident value of macro-NUE for a specific crop, experiments carried out in different years and locations, and different climate and soil conditions should be considered. In general, two processes governing macro-NUE are recognized: the efficiencies of acquisition and utilization of the nutrient. Efficiency of nutrient acquisition (ENA) expresses the capacity of plant roots to acquire the nutrient from the soil and is commonly referred as the percentage of the nutrient of interest present in the soil that is acquired by the plant. ENA is directly determined by the uptake activity and capacity to explore the soil of the root system, which involves the active transport across plasma membrane (PM), and changes in growth and development of roots (primary and secondary) in response to the nutrient availability. Efficiency of nutrient utilization (ENU), expresses the fraction of the nutrients already in the plant that is used to produce the desired biomass or grain yield. Evaluation of ENU must be performed on a range of internal nutrient concentrations below the optimum concentration. Considering the more dynamic concept, values are calculated by evaluating the application of fertilizers at the precise period during which plant growth is compromised by nutrient limitation, different from the traditional evaluations that consider continuous growth under supraoptimal and suboptimal nutrient levels. Additionally, the factor of nutrient loss (by leaching from aerial and belowground plant parts) should be taken into consideration because it provides complimentary information regarding nutrient economy, provides a compelling and comprehensive paper of all these concepts.33 As macro-NUE critically impacts the management of essential nutrients naturally present in the soil (as well as those added as fertilizer), water and soil use, and all the required resources in agricultural systems, it is a very important variable for evaluating crop production in general, and particularly when crop improvement is the goal. Although wide variation has been observed, several authors report a negative correlation between ENU and ENA in different crop species.35,36 However, as substantial linkage has been shown between Quantitative trait loci (QTL) mapped for ENU and ENA, improving both components simultaneously appears to be possible.35 In this context, two complimentary strategies are devised: (1) improving ENA that must include both mechanisms for nutrient uptake and transport and the modulation of the RSA, and (2i) improving ENU by which the acquired elements are utilized to generate vegetative biomass or edible parts of plants. These strategies will allow the development of very efficient plants, with improved nutrient uptake capacity and high internal utilization, which will contribute, with high- and low-input agricultural systems. Improving ENA and ENU for the most critical macronutrients, P, N, and K, in crop plants is a hard and complex issue, which is being tackled by traditional breeding and molecular directed strategies. Traditional breeding programs are based on trial and error strategies, that spite being slow and require intensive phenotyping programs of populations derived from crosses or native cultivars, they have produced promising results. However, the development of tools, such as plant transgenesis and genome editing, sophisticated phenotyping equipment and software, decreasing prices of genomic sequencing and the development of genome wide association mapping (GWAS), and the generation of high-quality knowledge in numerous fields (plant biology, molecular biology, and plant imaging) as well, are providing the bases to design more effective and efficient directed breeding programs. To date, substantial advances in our understanding and various efforts for improvement of ENA and ENU have been made. In the following sections, relevant information regarding genetic and molecular determinants involved in both processes for Pi, NO −3 , and K+ nutrition will be presented.

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MOLECULAR AND GENETIC BASIS OF USE EFFICIENCY OF PHOSPHATE, NITRATE, AND POTASSIUM The root system uptake activity and capacity to explore the soil are the major determinants of ENA, whereas nutrient assimilation and homeostasis directly impact ENU. High-throughput profiling methods have been used to unveil and analyze global changes in gene expression in response to nutrient deprivation. Global expression data has revealed that there is a coordinated induction and suppression of responsive genes, a differential expression profile in roots and shoots during either starvation or resupply, and the existence of specific set of genes involved in either local or systemic transcriptional responses, and in RSA changes. Together with the study of plant mutants, these data are providing an important insight to identify key components controlling nutrient acquisition and utilization, which will be presented in the following sections. Even though all these components are related to each other and finally impact plant development and reproduction, information will be presented in three sections: mechanisms for nutrient uptake and transport, modulation of the RSA, and regulation of nutrient assimilation and mobilization.

MECHANISMS FOR NUTRIENT UPTAKE AND TRANSPORT Regulation of phosphate uptake How plants acquire Pi from the soil, is an issue widely studied in several plant species including important crops, such as rice (Oryza sativa), wheat (Triticum aestivum), soybean (Glycine max), and maize (Zea maize). To date, abundant data regarding the two-known mechanism: the arbuscular mycorrhizal (AM)-assisted, and the direct phosphate transporter (PT)-mediated uptake pathways, is available (Fig. 1.1). Both mechanisms could provide complimentary approaches to improve efficiency of phosphate acquisition (EPA) and utilization (EPU). In the AM-assisted uptake pathway, Pi is taken up via fungal hyphae and then released into plant cells.37,38 Specific PTs act to transport Pi during this association (i.e., MtPT4, OsPT11, and OsPT13),39,40 therefore, they are important targets for plant breeding and to maximize its benefits. The use of mycorrhizal fungi has been suggested for improving the availability of Pi in the soil, however, the amount of Pi obtained through this pathway in most cases is insufficient to achieve high yields (Fig. 1.1). The direct PT-mediated uptake pathway has the major contribution to crop Pi-nutrition in intensive agriculture fields. Plants PTs allow the entry of Pi mainly in the form of H 2 PO −4 and to a lesser extent as HPO −42. Roots take up and concentrate Pi from the low concentration present in the soil solution of around 0.1–100 mg P L−1 in xylem sap.21,24 Once inside the plant, Pi moves simplastically from the root surface to the xylem, and then rapidly transported to the above ground organs. Phloem flow moves Pi from senescent tissues to growing tissues and then to the root system again.3,17 Plants have both a constitutive low-affinity and an inducible high-affinity Pi-uptake systems mediated by PTs. Expression of most high-affinity PTs is activated when external Pi concentrations are below 10 µM. PTs have been grouped into four gene families in plants: Pht1, Pht2, Pht3, and Pht4.3,41 The Pht1 (Phosphate transporter1) gene family comprises high-affinity PTs directly associated with Pi uptake in the root–soil interface. Pht1 genes, such as OsPT2, OsPT6, OsPT3, and OsPT7 in rice, MtPT1 and MtPT2 in Medicago (Medicago truncatula) play an important role in Pi uptake as they are induced in the root system by low-Pi availability. In the case of maize, ZEAma;Pht1;1, ZEAma;Pht1;2, and ZEAma;Pht1;3 were proposed as components of the PT-mediated uptake pathway.3,42 PTs belonging

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to the Pht2, Pht3, and Pht4 gene families are classified as low-affinity PTs, which in general are proposed to participate in Pi distribution inside the plant and are located in the cellular and organelle membranes.3 Although members of PTs belonging to these four families have been identified in several plant species, further investigation is needed to expand our knowledge about their precise function in Pi homeostasis.3,41,43 The Pi efflux transporter PHO1 (Phosphate 1) plays a major role in Pi homeostasis, and exciting results on its activity have been reported recently (see later for a detailed information).44,45 Molecular signaling pathways controlling the expression and activity of PTs to regulate Pi homeostasis in plants are complex and still lacking of information. However, conserved mechanisms regulate the expression of PTs genes in different plant species. A conserved master regulator is the transcription factor (TF) PHOSPHATE RESPONSE 1 (PHR1) (see later for a more detailed description), which controls transcriptional activation of a large subset of Pi-starvation responsive genes (PSR) including those belonging to the Pth1 gene family.46–48

PHR1: a master regulator PHR1 was the first TF identified as a central positive regulator of the Pi-starvation response in a vascular plant. PHR1 contains a MYB domain specific for plants and a C-terminal coiled coil domain, and it is not responsive to Pi levels by itself. Independent of the Pi-status of the plant, PHR1 is in the nucleus, where it binds to DNA as a dimer to an imperfect palindromic 8-base pair sequence (GNATATNC) denominated the PHR1 binding sequence (P1BS) motif, which is present in the promoter of many PSR genes. PHR1 was initially identified in Arabidopsis (Arabidopsis thaliana), subsequently in rice and common bean, and more recently, two putative PHR orthologs, and three PHR1 homologous genes were identified in maize and wheat.49,50 The importance of PHR1 in the regulation of Pi-starvation adaptive responses was revealed by analyzing the expression of PSR genes in phr1 mutant where a significant loss of responsivity was observed. In rice, OsPHR2, an AtPHR1 homologous, is a key regulatory component because OsPHR2-overexpressing plants over-accumulate Pi under Pi-sufficient conditions, which correlated with the increased expression of OsPT9.51 PHR1-LIKE 1 (PHL1) is a phylogenetically close relative of PHR1 that displays functional redundancy in controlling gene expression as demonstrated in the double phr1phl1 mutant, where almost 70% of PSR reported genes showed a significant deregulation.52 Together, these two TFs are key integrators of both specific and generic Pistarvation responses and therefore represent targets to improve EPA and EPU. Recently, the nuclear proteins SPX1 and SPX2, that share the SYG/Pho81/XPR1 (SPX) tripartite domain with yeast Pi-sensors and with several Pi-starvation signaling plant proteins, were characterized in Arabidopsis and rice as Pi-dependent competitive inhibitors of PHR1 activity by binding to its DNA binding domain.53–55 SPXs proteins bind to PHR1 when the internal levels of Pi are high, inhibiting its activity as a TF, and separate from PHR1 when levels of Pi are low, allowing the transcriptional activation of PSR genes.53 The activity of the PHR/SPX module was demonstrated in rice where the expression of OsPT2 and OsPT8 genes was found to be upregulated under Pi-sufficient conditions in a spx1 rice mutant.54–56 Recent data indicate that levels of specific pyrophosphate inositol polyphosphates (PP-InsP) rather than Pi itself are the molecules that are sensed to measure Pi levels. Genetic, biochemical, and crystallographic analysis of SPX proteins suggests that SPX domains function indeed as intracellular receptors for InsPs.57 Therefore, SPX1 and SPX2 act as Pi status dependent inhibitors of TFs, such as PHR1 during Pi replete conditions.53,58 The importance of the sensing cellular Pi levels in the setting of Pi-starvation adaptive responses implicates that this signaling pathway is a potential target for the improvement of EPU in crop plants.

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A finely controlled network of nitrate transporters and sensors

NO −3 uptake is an essential process for plant growth and productivity therefore, expression of genes involved with it must be finely regulated and considered to improve efficiencies of NO −3 acquisition (ENiA) and utilization (ENiU) (Fig. 1.1). As the main provider of N for plants, NO −3 -uptake system will be revised. In the heterogeneous soil, NH +4 and NO −3 are taken up across the PM by high- and low-affinity transporter systems located in epidermal and cortical cells that ensure the intake of adequate levels over a wide range of concentrations. Both transporter systems are composed of constitutive and NO −3 inducible members. To date, four nitrate-transporter gene families are known, of which NRT1/PTR and NRT2 are the main gene families responsible of NO −3 uptake, the first also comprising transporters involved in NO −3 efflux (NAXT1 and NRT1.5).59 NRT1.1 (also called Chlorate resistant 1, CHL1) belongs to the NRT1/PTR gene family and together with members of the NRT2 gene family, are high-affinity transporters particularly important under low NO −3 availability. In NO −3 deprived Arabidopsis plants, AtNRT2.4 and AtNRT2.5 are responsible for NO −3 uptake from the soil, while AtNRT2.1 plays a role in apoplastic NO −3 absorption. NRT1.1 and NRT2.1 also function as NO −3 sensors. Depending on NO −3 concentrations in the soil, NTR1.1 switches between a transporting to a signaling activity via a phosphorylation mechanism mediated by calcineurin B-like-interacting protein kinase 23 (CIPK23).22,60 NRT1.1 transceptor-dependent gene regulation is quite complex. For instance, NRT1.1 can upregulate NRT2.1 in response to short-term NO −3 -induction, and down-regulate it under prolonged high NO −3 levels.61 The importance of NRT2.1 as a part of the inducible transporter NO −3 -transporter system was demonstrated in nrt2.1 mutants that lack up to 75% of their high-affinity NO −3 uptake activity. To be active, NRT2.1 forms a functional unit with NAR2.1 (nitrate accessory protein2.1, also called AtNRT3.1), which plays an important role in both constitutive and inducible high-affinity transport systems.62 Expression of NRT2.1 is upregulated by NO −3 and sugars, and downregulated by N-assimilation products (e.g., glutamine).14,63

A complex network of potassium transporters and channels

As the most abundant cation in plants, maintaining K+-optimal levels in cells is essential for the activity of many cytoplasmic enzymes and the numerous physiological cellular processes requiring K+. This task is accomplished by a highly complex network of transporter and channel proteins, controlled by precise electrochemical cell conditions, and responsive to external K+ concentrations.8,64 This complex network comprises both high- and low-affinity mechanisms and is constituted by three gene families that encode K+-channel proteins: Shaker, TPK (tandem-pore-K+), and Kir (K+-inward rectifier)-likeencode K+; and four gene families encoding K+-transporters: KUP/HAK/KT, HKT (high-affinity-K+ transporters), NHX, and CHX (Cation/H+ exchangers).65 Some of these transporters function as Na+transporters, Na+/K+-symporters, Na+/H+-antiporters, and K+/H+-antiporters. All these systems possess different energetic coupling, affinity, and selectivity for K+, and voltage sensitivity.5 Thus, K+ transport has a high level of complexity and, therefore, their manipulation attempting to improve efficiency of K+ acquisition (EKA) could have a wide range of possible consequences on cell functions. Numerous K+-transporters and channels have been identified in Arabidopsis, rice, maize, wheat, tomato, and potato, however, functional analysis to corroborate their activity has been demonstrated in plant cells only for some of them.66,67 The Arabidopsis K+-Transporter1 (AKT1) channel (and its homologues OsAKT1, ZMK1, TaAKT1, LKT1, and SKT1) and the HAK5 transporter (one of the first identified in plants), belonging to the Shaker and KUP/HAK/KT families, respectively, have been determined

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as the main components for K+ uptake under restrictive conditions.68–70 In general, K+-transporters are preferentially regulated at the transcriptional level, whereas K+-channels are regulated at the posttranslational level.71 In addition, several signal components, such as Ca2+, reactive oxygen species (ROS), phytohormones [i.e., ethylene (ET), jasmonic acid (JA)] and membrane potential are also involved in the regulation of specific K+-transporters. Under K+ restricted conditions, the H+-ATPase AHA2 activity is enhanced and contributes to changes in membrane potential trough extracellular acidification. Transcription of HAK5, KEA5, KUP3, CHX13, and CHX17 genes encoding for K+-transporters, is induced under K+-deficiency in Arabidopsis.5 HAK5 induction is also dependent of ROS, ET and the activation by the AP2/ERF 2.11 TF (RAP2.11).72–75 Indeed, RCI3 (rare cold-inducible gene 3), a peroxidase from the type III peroxidase family involved in ROS production in Arabidopsis roots, which expression is responsive to K+ deficiency, is essential for the induction of HAK5 expression under K+-deficient conditions, and together with RBD2, another oxidase gene, upregulate also the WRKY9 TF.74,75 Using an Arabidopsis TF box (TF Full length Over-expressor) library, 27 TFs were identified that activate under K+-deficiency the expression of HAK5 by a direct interaction with its promoter. Four of them (DDF2, JLO, TFII-A, and bHLH121) were further characterized and found to be involved in responses to different factors, such as auxins (AUXs) and NaCl-stress.76 It has been proposed that like NO −3 sensing, in which CHL1 acts as a both transporter and sensor depending on its phosphorylation status, AKT1, which is primarily expressed in root epidermal cells and located in PM, may acts also as a sensor in K+ metabolism. In contrast to CHL1 that is regulated by NO −3 availability, AKT1 is not transcriptionally induced by K+ deficiency. The low-K+ inducible CIPK23 that phosphorylates also AKT1 and the AIP1 phosphatase that dephosphorylates AKT1, have been proposed to form a molecular complex that modulates the switching of its transporter/receptor activity.77,78 Thus, AKT1 may senses low levels of K+ and then affect the membrane potential controlled by the AHA2 H+-ATPase. Several components involved in the posttranslational regulation of K+-transport in response to its deficiency have been identified to date. CBL (calcineurin B-like) proteins, which form complexes with serine/threonine kinases called CIPK proteins, are key regulators of K+ uptake under K+-deficiency stress.79,80 Apparently, the CBL-CIPK interaction determines the specific localization of the CIPK-CBL complex to the different membranes where the K+-channels are functional. Many different CLB-CIPK combinations are active and regulate diverse biological processes in plants; CLB1 and/or CLB9 interact with CIPK23 to activate AKT1 by phosphorylation to enhance K+ uptake, while AKT2 activity is regulated by CBL4-CIPK6 complex.78,79,81 CBL10 alone, whose expression is inhibited under K+-deficiency, negatively regulates AKT1 activity by a direct interaction.5,79–81 K+ itself acts as a negative regulator of AHA2 activity by binding to the C-terminus of the protein. In addition, the formation of heterotetrameric channels (i.e., AKT1-AtKC1 complex in Arabidopsis) is another mechanism to negatively regulate K+-channels, such as AKT1, AKT2, KAT1, and KAT2.5 This mechanism is of particular importance because each functional subunit structured as well as their individual components, are able to respond to a wide range of K+ conductance.80 For instance, AKT1 can form a functional homocomplex whereas KC1 does not function as a K+-channel alone. The AKT1-KC1 is upregulated by CLB–CIPK complexes as well.79,82,83 14-3-3 proteins have been involved in the regulation of several cellular processes and more recently in K+-channels activity by a protein–protein interaction. The activity of KAT1 and TPK1 K+-channels activity is increased by their interaction with 14-3-3 proteins, which correlate with the transcriptional activation of some 14-3-3 encoding genes under K+-deficiency.84–87 The direct interaction between

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GF14-6, a maize 14-3-3 protein, and Arabidopsis KAT1 to enhance K+ currents, was demonstrated in vitro using Xenopus oocites.86

MODULATION OF THE ROOST SYSTEM ARCHITECTURE Plasticity of the root system to phosphate availability Pi is an element with very low mobility in the soil, which is predominantly fixed in upper soil layers. Therefore, to efficiently exploit and to take advantage of these deposits, plants trigger Pi-limitation strategies, which contribute to the economy of the global system. Root traits potentially involved in higher EPA have been widely studied in several crops and model plants, such as Arabidopsis, maize, common bean, soybean, rice, and tomato (Solanum lycopersicum) (Fig. 1.2). An important contribution of phytohormones (i.e., AUX, ET, JA, cytokinins (CK), gibberellins, and strigolactones), sugars, and even the redox status of the root meristem have been found to control them under low-Pi availability. Common bean and white lupin (Lupinus albus), are used frequently as model plants to study in detail RSA modifications that help plants to use Pi more efficiently. Characteristically, white lupin and wild species of the Proteaceae family, develop proteoid roots (dense clusters of short side roots) which are most prominent in the upper soil layer and contribute to mobilize Pi through the exudation of organic acids rather than by scavenging through root extension.88–90 Organic acids exudation has been associated with enhanced activity of certain enzymes (i.e., malate dehydrogenase and citrate synthase) and specific transporters [i.e., ALMT (aluminum-activated malate transporter), MATE (multidrug and toxic compound extrusion)] in proteoid roots.91 Typically, when common bean develops under low-Pi availability, a shallower and broader root system is observed. This is the result of an increased adventitious root formation and a change in the angle of basal roots leading to an increased dispersion of lateral branching89,90,92 (Fig. 1.2). Additionally, the basal root whorl number phenotype has been also associated with enhanced Pi acquisition. In fact, Pi-efficient genotypes of common bean present these characteristics with an important variability in the angle of basal roots, typically known as “topsoil foraging”. Increase in axial root lengths, without increased lateral branching, has also been found in maize, and has been interpreted as an explorative behavior for soil patches enriched in Pi where LR formation then would become functional.93 Root hairs (RHs) are also an important component of the efficiency of Pi-uptake, as they can contribute with up to 90% of the Pi acquired by plants. Increased RH length and longevity has been predicted to have the greatest influence for high Pi-uptake efficiency phenotype.94 This has been demonstrated in field trials of two barley cultivars, Zita and Salka, with contrasting RH length. In these experiments the Salka cultivar, with longer RHs, produced more shoot and biomass than the other genotype.95 An interesting conceptual modeling demonstrated mathematically that globally these root traits are indeed the best possible combination to have an efficient Pi-scavenging root system which must be considered to develop Pi-efficient crops.94 Several TFs have been identified as key components in modulating RSA in response to Pi-deprivation. The ZAT6 TF was identified as a negative regulator of RSA; Arabidopsis ZAT overexpressinglines had shorter PR, however they present longer LRs and higher Pi content in shoots and roots under both Pi-deficient and sufficient conditions.96 ZAT6 expression is induced under Pi-deprivation, and it is possible that its expression is regulated by an AUX-dependent pathway as its expression is reduced in AUX-resistant mutants.96 TIR1 (transport inhibitor response1) and other AUX receptors [auxin signaling F-Box binding1-5 (AFB1-5) and the TFs ARF7 (auxin response factor7)] and ARF19 have been

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FIGURE 1.2  Targets to Improve Efficiencies of Phosphate Acquisition and Utilization Multiple genetic and molecular components participate to regulate efficiency of phosphate (Pi) acquisition [efficiency of phosphate acquisition (EPA), blue box] and utilization [efficiency of phosphate acquisition utilization (EPU), red box] in plants. Components identified to date create a complex network which controls Pi uptake, transport and remobilization, and determine also root morphology, Pi content in seeds, photosynthetic efficiency and the release of Pi fixed in the soil. More recently, epigenetic changes have been found to have a crucial role in transgenerational acclimation to Pi-starvation (i.e., H2A.Z deposition). The manipulation of these components can contribute to generate improved plants with enhanced EPA and EPU. ARF12, Auxin response factor 12; PSTOL1, phosphorus starvation tolerance1.

proposed to increase AUX sensitivity in pericycle cells in response to Pi-limitation.97 Overexpression of TIR1 in Arabidopsis leads to 75% more LRs than control plants.98 Recent studies have revealed that epigenetic modifications (e.g., DNA methylation, histone variants and modifications, positioning of nucleosomes, and noncoding RNA) also play an important role in the adaptive responses to Pi-starvation by regulating gene expression. Recent data indicates that proper

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deposition of H2A.Z, a histone variant, modulates the transcription of several PSR genes in Arabidopsis. arp6, an Arabidopsis mutant defective in H2A.Z deposition, shows missregulation of PSR genes that correlates with the presence of multiple Pi-starvation-related phenotypes, such as increase in the density and length of RHs, shortening of primary root (PR) length and increased phosphatase activity under Pi replete conditions99 (Fig. 1.2). Studies on rice and Arabidopsis showed that DNA methylation also correlates with expression changes of PSR genes during Pi-starvation.100,101 Similarities between the set of genes differentially methylated and differentially expressed between rice and Arabidopsis suggests that the regulation of PSR genes via DNA methylation is conserved among plants. These epigenetic modifications have the potential to be transmitted to the next generation and probably provide an advantage to specific stress conditions. QTLs associating root traits with EPA have been also identified in crops, such as maize, common bean, and rice. In common bean, three major QTLs explaining 58% of the phenotypic variations in EPA were associated with the basal root whorl number. In maize, using the B73 (nonefficient) and Mo17 (efficient) genotypes, was possible to identify several other QTLs; one associated with LR number, five controlling LR length, six linked with Pi seed content, and one associated with RH length.102 In rice, by crossing the Indica landrace Kasalath (tolerant to Pi-deficiency) with the Japonica cultivar Nipponbare (intolerant to Pi-deficiency), was possible to identify the Pup1 (phosphorus uptake1) QTL. The gene responsible for the effect of Pup1 was identified as the PSTOL1 (phosphorus starvation tolerance1) gene, an early root growth enhancer, whose introgression in modern rice varieties produced spectacular results increasing Pi-uptake by 170% and grain yield by 250%.103,104 Additionally, a primary role of expansins in root architecture modifications and Pi economy has been established. In Arabidopsis, the overexpression of a β-expansin encoded by the GmEXB2 gene from soybean leads to an increased Pi-uptake under both low- and high-Pi levels, which was related to a higher root cell division and elongation.13,105

Root architecture responses to nitrate availability

Changes in RSA in response to NO −3 availability are particularly interesting because NO −3 distribution has contrasting effects in several plant species. A local supply of NO −3 in the form of patches, promotes LR elongation in the zone directly in contact with the patch whereas a high and uniform NO −3 supply causes an inhibitory effect on LR development.2 Recent data indicate that when a uniform source of NO −3 is supplemented to the whole root system, the LR density increases. Therefore, systemic signals communicate internal N-status of the plant, which is translated into LR growth away from the site of NO −3 perception. Nutritional regulation of root architecture by external NO −3 is evolutionarily conserved between dicots and monocots, and ANR1 (Arabidopsis nitrate-regulated1)-like MADS-box genes are a key component in this process. A signaling pathway involving NRT1.1 and the ANR1 TF, which are expressed in root tips of PR and LRs, positively regulate the meristematic activity at the LR tip and LR elongation in response to external NO −3 .13,106,107 Alterations in PR and LR development independently of NO −3 - uptake and availability presented by nrt1.1 mutants was associated to low transcription of ANR1. Enhancer trap and Dex-inducible overexpressing lines confirmed that ANR1 acts as a positive regulator of LR initiation and growth, being more responsive in the presence of NO −3 . Interestingly, nrt2.1 mutants present affected LR initiation independently of its transport activity, supporting its role as a sensor or signal transducer in an N-dependent pathway.108 Plasticity of root branching in response to NO −3 availability is also strongly influenced by phytohormones, especially for UAX, CK, and abscisic acid (for a detailed review of hormonal control of

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N-metabolism see Ref. [63]). NRT1.1 not only acts as a NO −3 -transporter and sensor to NO −3 responses, but also as a possible facilitator of AUX transport to promote LR growth under low-NO −3 conditions.106,109 AFB3, an AUX receptor, was identified as a very important component regulating changes in RSA in response to NO −3 availability. AFB3 is induced by NO −3 in roots and downregulated by N-metabolites via miR393 which targets AFB3 transcripts for degradation, affecting PR and LR growth by altering AUX perception.110 AFB3 regulates a gene network under control of the NAC4 TF.110 Additionally, the regulatory module miR167/ARF8 was also identified as an important component in regulating the ratio between initiating and emerging LR in Arabidopsis.111 The tryptophan aminotransferase related 2 (TAR2) that is expressed in the pericycle and the vasculature of the mature root zone near the root tip, is also a central component for reprogramming RSA in response to low-NO −3 conditions.112 Several systemic signals contribute to regulate N assimilation trough metabolic adaptations and root morphologic changes. High nitrogen insensitive9 (HNI9) was found to mediate systemic repression of NO −3 -uptake through the root system, which was associated with changes in histone methylation.113 Interestingly, the small C-terminally encoded peptides (CEPs) upregulate systemically NO −3 -transporters in roots. CEPs are produced by roots under NO −3 -starvation and are translocated to the shoot, where they interact with the leucine-rich repeat receptor kinases CEP receptor1/2 (CEPR1/2). Recently, the CLE (CLAVATA3/ESR-related) peptide-signaling pathway and the receptor of CLE peptides CLAVATA1 (CLV1) module was found as an essential mechanism to control root branching under NO −3 limitation.114 This module negatively regulates LR development as overexpression of CLE1, 3, 4, and 7 represses LR primordia development, whereas in clv1 mutant the progressive growth of primordia from the PR is observed. All these regulatory networks that require fine-tuning of gene expression levels at the transcriptional and posttranscriptional levels are potential targets for genetic engineering and the new breeding technologies.

Root architecture responses to potassium availability

As for the case of Pi and NO −3 , K+-deficiency affects the growth and architecture of the root system as this cation provides osmotic pressure and turgor which contribute to cell expansion in the elongation zone.115 Owing to K+ importance in numerous physiological plant processes, plants growing under K+-deficiency conditions exhibit stunted growth and poor development. Typically, plants exhibit similar root phenotypic responses as described earlier for NO −3 and Pi-deprivation, including a significant decrease in PR growth, increase in LR length and number, and an increased RH elongation.72 These changes are critical adaptation processes that enable plants to survive during periods of severe nutrient deficiency, and are clearly observed after 28 h of exposure to K+-deficiency in Arabidopsis.72 Increased length of RHs is considered a landmark of K+-deficiency responses, and has been observed in several plant species including pea (Pisum sativum), red clover (Trifolium pratense), barley (Hordeum vulgare), rye (Secale cereale), perennial ryegrass (Lolium perenne L.), alfalfa (Medicago sativa L.), and oilseed rape (Brassica napus oleifera). RH length correlates with the capacity of soil exploration; cereals that have longer RHs than dicots have a greater ability to explore the soil than legumes.116 Compelling evidence demonstrate that root changes in response to K+-deficiency are dependent on several factors, such as external NH +4 levels and phytohormones including ET, AUX, and CK. High toxic levels of NH +4 have been shown to repress root growth of plants, which is enhanced by K+-deficiency and reversed when K+ is resupplied. This behavior may be explained by competition and interference between these two cations during uptake by the root. High levels of NH +4 inhibit the transcriptional

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activation of K+-transporters genes including AtHAK5, HvHAK1, and CaHAK1, which are activated by K+-deficient conditions.33,117,118 However, the activity of AKT1-K+ channel is not NH +4 sensitive.119 The crucial role of ET and AUX in K+-deficiency root responses was demonstrated by studying ethylene response and auxin-resistant mutants. For instance, wild type plants treated with ET show a similar PR growth and RH elongation phenotype to that observed in K+-deprived plants, but this effect is not observed in etr1 and ein2 plants.72 Indeed, the production of ROS and, thus, the expression of HAK5 expression, are repressed in plants treated with ET, which are also partially eliminated in ethylene-insensitive mutants (e.g., ein2 and ctr1).72 Genes involved in ET production are induced by K+-deficiency which causes rapid increase in RT concentration.75 Under K+-deprivation, ET enhances AUX synthesis which accumulates in central cylinder cells in the distal elongation zone.120 TRH1, a K+ transporter belonging to the KUP/HAK/KT family localized in stele and epidermis, which also acts as an AUX efflux facilitator, may contribute to AUX redistribution.120 Characterization of trh1 mutant corroborated this data, as this mutation causes ectopic localization of the PIN1 AUX efflux carrier, affecting AUX transport and RH growth.121

REGULATION OF NUTRIENT ASSIMILATION AND REMOBILIZATION The central role of PHO1 in phosphate homeostasis The mobilization of Pi inside the plant, starting with uptake, loading into the vascular system and its translocation to growing organs (leaves, tiller, and seed), is closely related to EPU. Therefore, identifying molecular and genetic components controlling these processes is of vital importance. PHO1 identification established the basis of one of the most important pathways controlling Pi homeostasis in plants. PHO1 is essential for Pi translocation from roots to shoots, by facilitating Piefflux out of the cells and into the xylem vessel.45 PHO1 is a protein containing an SPX domain in its N-terminal region, which is the site of its regulation by PHO2. Its importance was initially demonstrated in null and insertion pho1 mutants, which present high Pi accumulation in roots and low levels of Pi in shoots, therefore suggesting a reduced Pi transport from root to shoot. pho1 null mutants have severely reduced shoot growth and seed yield. PHO1 orthologs have been identified in several plant species to date, including Brassica rapa, soybean, rice, maize, and Brachypodium distachyon. Recent experimentation in Arabidopsis has permitted to expand our knowledge on how PHO1 acts and, therefore, its potential to improve EPU in plants. Arabidopsis lines expressing low levels of PHO1 transcript or expressing the rice PHO1 ortholog in an Arabidopsis pho1 null mutant have normal shoot growth despite having low Pi levels in the shoot like those found in the pho1 mutant.44 These findings suggest that the drastic phenotypic growth response of plants to Pi-deprivation can be separated from the level of internal Pi status and that PHO1 acts as a part of the Pi sensory system active at the systemic level or that PHO1 participates in the transport of a systemic signal that regulate Pi-deprivation responses. It is possible that PHO1 participates in systemic signaling by regulating the transport of miRNAs or mRNAs, however, further experimentation is necessary to demonstrate this hypothesis.3 These findings open the possibility of developing plant varieties that could be fertilized with lower levels of Pi without having a strong negative effect on growth and productivity, however, we still need more information about the PHO1 mode of action and the potential Pi-sensing that could control the phenotypic responses to Pi-deprivation. Moreover, because much of the Pi is in the form of organic Pi-esters, either in the soil or in the plant as a natural consequence of Pi incorporation (i.e., Pi accumulation in seed), the liberation of Pi

 Molecular and genetic basis of use efficiency

17

from these compounds, and the decrease and reallocation of Pi content in seeds may impact PUE and PUA (Fig. 1.2). In this context, a range of metabolic modifications occurs in the plant which include the mobilization of Pi within plant tissues, for example, recycling Pi from mature/senescing plant parts to actively growing organs and tissues, and efficient reutilization of Pi from vacuoles that have a buffering function in storing Pi when is in excess in the cytoplasm (Fig. 1.2). Multiple PTs function during internal mobilization, and are upregulated specifically by Pi-deprivation. Specific types of phosphatases also play important roles in Pi reallocation within the plant and liberating Pi from external organic sources. In maize var. B73, 33 candidate genes encoding PAPs were identified, some of them induced only under Pi-starvation.122 Similarly, LaSAP1 phosphatase expression is enhanced under low-Pi in roots of white lupin.123 Efficiency of internal Pi utilization is complex and difficult to assess, thus is being approached by using genomic techniques, such as metabolomics and transcriptomics.

Nitrate assimilation and mobilization

After uptake, NO −3 can be metabolized directly in the root, stored in the vacuole, or transferred to the shoot. It is loaded into xylem and subsequently distributed through the action of NTR1 and NTR2 transporter families. Efficiency of NO −3 utilization (ENiU) starts with reduction of NO −3 to nitrite (NO −2 ) through the reaction catalyzed by the cytosolic enzyme nitrate reductase (NR), then NO −3 is imported into the plastids in roots or leaves to be reduced to NH +4 by the enzyme nitrite reductase (NiR). Finally, the resulting NH +4 is then incorporated into amino acids through the GS/GOGAT pathway.14,23 These enzymes have been identified in several plants species and their importance in plant productivity has been shown for maize, rice, wheat, and sorghum (Sorghum spp.), as they have key roles in grain filling, determining kernel size, and yield.124 Therefore, modulation of the activity of these enzymes should be considered to improve ENiU. ENiU is directly influenced by the reduction of NO −3 to NH +4 , followed by NH +4 assimilation, and the remobilization and storage of NO −3 , whose regulation is influenced by several factors. Typically, NO −3 is translocated from older to younger leaves, which receive more direct sunlight. Transcription of NR-encoding genes are induced by light and carbohydrates, thus when photosynthates are available, NO −3 assimilation is improved as well as the production of amino acids. Asparagine synthetase (catalyzes the formation of asparagine and glutamic acid from glutamine and aspartate), together with GS, plays a crucial role in primary N-metabolism. Signal transduction pathways controlling NO −3 these processes are highly complex and far from being clearly elucidated. NRT1.1, CIPK8 and CIPK23, and several TFs (AtSPL9, AtTGA1/4, AtNLP6/7, and LBD37-39) are important regulatory components of the NO −3 -response.125–128 Among them, NLP7 is proposed as master regulator of the early NO −3 response because in Arabidopsis nlp7 mutants the expression of NO −3 -transporters (NRT2.1 and NRT2.2) and NR-encoding genes (NIA1 and NIA2) are reduced.129 Amino acid transport is also an important ENiU component as it provides N where the plant needs it to sustain its physiological functions. Amino acid transporters are key regulators in plant metabolism since their activities influence growth and development.23 In addition to NRT and AMT gene families which encode NO −3 and NH +4 -transporters, several others, such as amino acid transporters, amino acidpolyamine-choline transporters, and amino acid/auxin permeases are directly involved in amino acid transport in plants.130 However, although some components have been identified, the complete amino acids transport system has not been fully elucidated. AAP1 and NRT1.6, localized in silique and seeds in Arabidopsis, are involved in seed development and filling, whereas AAP2 and AAP6 which mediate transfer of amino acid from the xylem to the phloem impact seed quality because both contribute to

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NO −3 and protein seed content.130,131 NRT1.5 participates in NO −3 loading into the xylem and NRT2.4, NRT 2.3a, and NRT2.5 are involved in root-to-shoot NO −3 transport and remobilization under N-limited conditions.59,132–134 Recently, 320 publicly available microarray data were analyzed to identify gene networks of N-transporters and to elucidate their possible biological roles.135 The authors of this work reported several important aspects: (1) sets of N-transporters are tissue-specific, (2) coexpression networks corroborated the relationships between N-transport and metabolic processes, such as water use and H+-ATPase activity as well as a coordinated regulation between phenylpropanoid biosynthesis and carbohydrate metabolism.135 These data provide valuable information for crop improvement trough the simultaneous manipulation of several genes or even of gene clusters.

Potassium homeostasis

Regulation of K+ homeostasis is an essential process mediating plant adaptive responses to abiotic and biotic stresses, which requires also the participation of a complex set of transporters and channels controlling K+ distribution between the cytosolic and the vacuolar pools. One remarkable component is the outward-rectifying Stelar K+-outward rectifier (SKOR) sensitive to K+, that is expressed in the root stele and is considered as a critical component for the long-distance distribution of K+ from roots to shoots. Whereas the weakly-rectifying AKT2, and inward-rectifying K+-channel in Arabidopsis thaliana1 (KAT1), KAT2, and KAT 2/3 channels, facilitate the loading and unloading in phloem tissues in the root system. Several other channels, such as GORK, SPIK, and TPK4 carry out specific tasks in guard and pollen cells, whereas TPK1, NHX1, and NHX2 contribute also to K+ homeostasis in higher plants.5,136,137 The activity of these proteins is strongly influenced by different factors including cellular voltage, external concentration of K+, and phytohormones. It has been proposed that SKOR can sense intracellular and stellar apoplastic K+ concentrations trough the S6 and pore domain in its C-terminal region. AKT2 currents are induced by the interaction with CLB4/CIPK6 complex interaction, which is enhanced under K+ deficiency, and reduced by the AtPP2AC phosphatase.78 Interestingly, AKT2 expression is responsive to K+ deprivation in rice, but not in Arabidopsis. In addition, the CIPK9–CLB3 complex was identified as an important molecular component to maintain cytosolic K+ homeostasis under K+ deprivation.16 At the metabolic level, it has been proposed that K+-dependent enzymes, such as pyruvate kinase, which is inhibited by low levels of K+,138,139 may also act as cytosolic regulators of K+-deprivation responses in plants, and therefore may have a potential to improve efficiency of K+-utilization (EKU).

IMPROVEMENT OF MACRONUTRIENT USE EFFICIENCY With the aim of improving ENU and ENA for the different macronutrients, numerous efforts have been made by manipulating candidate genes (Fig. 1.3). Transgenic approaches based on either overexpressing or using knockout mutations in protein transporters, TFs, specific enzymes, and other components are providing interesting information, however positive results are restricted to a few cases. Most of the plants produced were evaluated, except for a few cases, in laboratory and greenhouse conditions rather than in the field, and effective improvements under field conditions have not yet being produced. The existence of complex networks in transport systems with high- and low-affinity phases, different types of proteins (transporters and channels) working on, and the existence of common genes

 Improvement of macronutrient use efficiency

19

FIGURE 1.3  Development of Plants With Improved Acquisition and Utilization Efficiencies of Macronutrients Modern technologies provide exciting opportunities to study plant nutrition. Manipulation of the expression of molecular and genetic components regulating uptake, transport, remobilization, and assimilation of phosphate (blue boxes), nitrate (red boxes), and potassium (green boxes), as well as those involved in root system architecture, and important regulatory and epigenetic components (red dashed boxes) has the potential to improve acquisition and utilization efficiencies in plants. In addition, alternative components must be also integrated (blue dashed boxes). AKT1, Arabidopsis K+-Transporter1; AlaAT, alanine aminotransferase; ALMTs, aluminum-activated malate transporters; ANR1, Arabidopsis nitrate-regulated1; ENOD93-1, early nodulin93-1; MATEs, multidrug and toxic compound extrusions; NAR2.1, nitrate accessory protein2.1; PSTOL1, phosphorus starvation tolerance1; SKOR, Stelar K+-outward rectifier; TPK, tandem-pore-K+.

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indicate that there may exists multiple components determining macro-ENA and -ENU, each of them active under specific conditions. Likewise, the complexity of the molecular and genetic basis of nutrient uptake, assimilation, and homeostasis regulation, together with the diversity of agro-environments with changing conditions (i.e., multiple and variable stresses, soil properties, and microbial activity), and influenced by human activities suggest the need of more integrated and reasoned solutions. Compelling evidence suggest that membrane transporters are the key targets for improvement of nutrient and water use. Some transporters from the aluminium-activated malate transporter and multidrug and toxic compound extrusion families have been found to confer aluminium (Al3+)-tolerance, and improve yields in barley, maize, and sorghum in acid soils.140 Interesting results have been also observed with HKT K+-transporters, which also act removing excess of sodium (Na+) from the xylem, thus favoring Na+-tolerance in plants. The introgression of HKT1;5 from Triticum monoccum (Na+-tolerant wheat relative) into Triticum turgidum ssp. durum (Na+-susceptible commercial durum wheat) resulted in 25% more grain when grown in saline fields.141 Encouraging results have been also obtained by manipulating KUP4 gene, associated to the distinctive K+ accumulation capability of alligator weed (Alternanthera philoxeroides), which resulted in a general improvement of growth, the production of more siliques and a higher accumulation of K+ in Arabidopsis.142 Overexpression in rice of the endogenous early nodulin93-1 (ENOD93-1) gene, potentially involved in amino acids transport, increased shoot biomass, and higher total amino acids content and higher seed yield especially under NO −3 stress.143 In addition, substantial evidence support the potential of OsPT6 to improve Pi nutrition.17 In the light of recent data, considering PHO1 and CHL1 roles as part of the sensory system, and their activity in nutrient transport and homeostasis, their manipulation may provide also strategies to improve plant nutrition. CHL1 is the point of regulation of many NO −3 transporters and together with NRT2.1, and other components, such as AFB3, ANR1, and mirR393 determine RSA in response to NO −3 availability. In this context, CKX, and some TFs (i.e., RAP2.11 and DOF1), whose overexpression in plants resulted in enhanced growth of the root system may contribute to enhance ENU and ENA.73 In addition, the overexpression of some enzymes, such as a barley AlaAT (alanine aminotransferase) in rice and tomato H+-PPases, LeAVP1D-1, and LeAVP1D-2, whose overexpression increased biomass and seed yield under NO −3 -limitation,144 and the development of larger shoots, root systems and fruits than controls when grown under Pi-deficient conditions,145 respectively, are potential candidate genes to improve plant nutrient utilization. The study of molecular components that are common points of regulation (i.e., CIPK23 and mir444) between the different networks of macronutrients assimilation can result in more robust basis for crop improvement (Fig. 1.3).146 Likewise, various QTLs related to macro-ENU and -ENA have been reported in many different crops and focused on representative traits, such as grain yields in maize and rice, shoot and root dry weight biomass/yield, tiller number, and RSA traits. One of the most remarkably genes identified through these practices is PSTOL1 in rice, with very promising results under experimentation in field conditions.17

CONCLUDING REMARKS AND FUTURE PERSPECTIVES Producing improved crop varieties that are more efficient to acquire nutrients from the soil and to assimilate them will help to optimize the global use of resources and decrease nutrient lost from agricultural fields. Addressing this task is a very complex process and the exploitation of information

 Concluding remarks and future perspectives

21

generated by high-throughput sequencing platforms, forward genetic screenings and the search for natural genetic variation is critical. During the last five decades, crops with enhanced ENA and ENU have been developed mainly by empirical knowledge and trough traditional breeding practices. Varieties currently available in the market were selected in and for nutrient-rich environments in which the development of above ground plant organs is more favored than that of the root system. Over the last few years, sophisticated platforms have been made and numerous characteristics can be documented with good precision. However, high throughput plant phenotyping, especially the study of root system development, at laboratory and field scales remains an important constraint for the identification of genes responsible for QTLs effects, carry out GWAS analysis and evaluate the effect of transgene and genome editing strategies. Gene expression data for an increasing number of crops under several conditions is also increasing rapidly, however, the challenge is to design appropriate mathematical and computational methods to analyze and integrate such data to effectively apply this knowledge for crop improvement. GWAS strategies are also powerful but will require the resequencing of hundreds to thousands of accessions to have enough statistical power to identify genes controlling major QTLs. Identification of candidate genes involved in ENA and ENU by studying genetic variation is confirming that natural mechanisms to cope up,with nutrient stresses are already present in a variety of organisms. A nonconventional example is presented by a metabolic pathway in some bacteria to use phosphite as a source of P. Recently, a novel technology based on the use of alternatives sources of P for plant nutrition was developed by expressing a bacterial gene encoding a phosphite oxidoreductase.17,147 This gene, allows transgenic plants to metabolize phosphite as the sole source of P to grow, providing additional benefits, such as an important decrease in P-fertilizer and herbicide application because of an effective control of weeds. The potential of this technology has been demonstrated in greenhouse and field conditions17,147 (Fig. 1.3). Some modern technologies are providing exciting opportunities for crop improvement. One of the most remarkable is CRISPR/Cas9 for genome editing and for which various successful efforts have been reported for economically important crops. These precedents make CRISPR/Cas9 highly suitable for crop improvement even for species with high ploidy genomes or with large life cycles. Through this tool, stable modifications that can be introduced into elite cultivars or hybrids, avoiding some of the characteristic disadvantages of traditional breeding, such as time-consuming backcrosses and the difficulty of pyramiding multiple traits into a single genotype. Genome editing allows the modification of several genes simultaneously facilitating the pyramiding of desirable alleles into a single variety in a short time. Moreover, allows more complex genetic modifications, such as the replacement of a gene from species into another or the incorporation of transgenes in precise and desirable location in the genome of a given crop. Although to date no significant genome editing efforts have been applied to enhance macro-NUE, it is expected that in the near future we will see an increasing number of reports describing the use of this technology to improve ENA and ENU. In view of potential beneficial effects of genes present in landraces, wild relatives and transgenes for improving macro-NUE and crop yield, a major challenge in future agriculture is to determine which of them can be pyramided, and the strategies to reduce the time of pyramiding these genes. Obviously, genome editing and transgene technologies could play a prominent role in achieving these goals. However, it remains to be determined whether regulatory agencies will facilitate or difficult the responsible use of these technologies, and whether these technologies will continue being exclusively developed by transnational companies, or small companies and research institutes would start to invest in this industry.

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44. Rouached H, Stefanovic A, Secco D, Bulak Arpat A, Gout E, Bligny R, Poirier Y. Uncoupling phosphate deficiency from its major effects on growth and transcriptome via PHO1 expression in Arabidopsis. Plant J 2011;65:557–70. 45. Secco D, Baumann A, Poirier Y. Characterization of the rice PHO1 gene family reveals a key role for OsPHO1;2 in phosphate homeostasis and the evolution of a distinct clade in dicotyledons. Plant Physiol 2010;152:1693– 704. 46. Rubio V, Linhares F, Solano R, Martín AC, Iglesias J, Leyva A, Paz-Ares J. A conserved MYB transcription factor involved in phosphate starvation signaling both in vascular plants and in unicellular algae. Genes Dev 2001;15(16):2122–33. 47. Schünmann P, Richardson AE, Smith FW, Delhaize E. Characterization of promoter expression patterns derived from the Pht1 phosphate transporter genes of barley (Hordeum vulgare L.). J Exp Bot 2004;55:855–65. 48. Nilsson L, Müller R, Nielsen TM. Increased expression of the MYB-related transcription factor, PHR1, leads to enhanced phosphate uptake in Arabidopsis thaliana. Plant Cell Environ 2007;30:1499–512. 49. Wang J, Sun J, Miao J, Guo J, Shi Z, He M, Chen Y, Zhao X, Li B, Han F, Tong Y, Li Z. A wheat phosphate starvation response regulator Ta-PHR1 is involved in phosphate signalling and increases grain yield in wheat. Ann Bot 2013;11(6):1139–53. 50. Calderón-Vázquez C, Sawers RJ, Herrera-Estrella L. Phosphate deprivation in maize: genetics and genomics. Plant Physiol 2011;156(3):1067–77. 51. Zhou J, Jiao F, Wu Z, Li Y, Wang X, He X, Zhong W, Wu P. OsPHR2 is involved in phosphate-starvation signaling and excessive phosphate accumulation in shoots of plants. Plant Physiol 2008;146(4):1673–2186. 52. Bustos R, Castrillo G, Linhares F, Puga MI, Rubio V, Pérez-Pérez J, Solano R, Leyva A, Paz-Ares J. A central regulatory system largely controls transcriptional activation and repression responses to phosphate starvation in Arabidopsis. PLoS Genet 2010;6(9):e1001102. 53. Puga M, Mateos I, Charukesi R, Wang Z, Franco-Zorrilla JM, de Lorenzo L, Irigoyen ML, Masiero S, Bustos R, Rodríguez J, Leyva A, Rubio V, Sommer H, Paz-Ares J. SPX1 is a phosphate-dependent inhibitor of phosphate starvation response 1 in Arabidopsis. Proc Natl Acad Sci USA 2014;111(41):14947–52. 54. Wang C, Ying S, Huang H, Li K, Wu P, Shou H. Involvement of OsSPX1 in phosphate homeostasis in rice. Plant J 2009;57:895–904. 55. Duan K, Yi K, Dang L, Huang H, Wu W, Wu P. Characterization of a sub-family of Arabidopsis genes with the SPX domain reveals their diverse functions in plant tolerance to phosphorus starvation. Plant J 2008;54:965–75. 56. Miura K, Rus A, Sharkhuu A, Yokoi S, Karthikeyan AS, Raghothama KG, Baek D, Koo YD, Jin JB, Bressan RA, Yun DJ, Hasegawa PM. The Arabidopsis SUMO E3 ligase SIZ1 controls phosphate deficiency responses. Proc Natl Acad Sci USA 2005;102:7760–5. 57. Pant B, Burgos A, Pant P, Cuadros-Inostroza A, Willmitzer L, Scheible WR. The transcription factor PHR1 regulates lipid remodeling and triacylglycerol accumulation in Arabidopsis thaliana during phosphorus starvation. J Exp Bot 2015;66(7):1907–18. 58. Wild R, Gerasimaite R, Jung J-Y, Truffault V, Pavlovic I, Schmidt A, Saiardi A, Jessen HJ, Poirier Y, Hothorn M, Mayer A. Control of eukaryotic phosphate homeostasis by inositol polyphosphate sensor domains. Science 2016;352(6288):986–90. 59. Wang Y-Y, Hsu P-K, Tsay Y-F. Uptake, allocation and signaling of nitrate. Trends Plant Sci 2012;1(8): 458–67. 60. Bouguyon E, Gojon A, Nacry P. Nitrate sensing and signaling in plants. Sem Cell Dev Biol 2012;23:648–54. 61. Gojon A, Krouk G, Perrine-Walker F, Laugier E. Nitrate transceptor(s) in plants. J Exp Bot 2011;62(7): 2299–308. 62. Laugier E, Bouguyon E, Maurie’s A, Tillars P, Gojon A, Lejay L. Regulation of high-affinity nitrate uptake in roots of Arabidopsis depends predominantly on posttranscriptional control of the NRT2.1/NAR2.1 transport system. Plant Physiol 2012;158:1067–78.

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CHAPTER

ROLE OF NUTRIENT-EFFICIENT PLANTS FOR IMPROVING CROP YIELDS: BRIDGING PLANT ECOLOGY, PHYSIOLOGY, AND MOLECULAR BIOLOGY

2

Martin Weih*, Anna Westerbergh**, Per-Olof Lundquist** *Crop Production Ecology, and Linnean Center for Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden **Uppsala BioCenter, and Linnean Centre for Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden

INTRODUCTION The efficient use of nutrients underpins food security and reduces losses of nutrients to the environment. Past developments have however, often resulted in the less efficient use of nutrients in agriculture; for example, the amount of nitrogen (N) fertilizer applied in cereal agriculture increased by a factor of 10 between 1960 and 1995, whereas global cereal production increased by a factor of 2.4 in the same period.1 While balanced nutrition is important, N in particular is fundamental in growing crops to feed the world now and in the future. Hence, the use of more N-efficient crops is likely to play a pivotal role in increasing or maintaining crop yields in the future, especially after climate change.2,3 For example, crop N use efficiency has been proposed to be an indicator of progress toward a goal to end hunger, achieve food security, improve nutrition, reduce pollution, and promote sustainable agriculture.4 At least 16 nutrient elements are required by plants,5 but most studies dealing with crop nutrition focus on N and phosphorus (P) because these elements are regarded as the most important in limiting crop growth and production.2,3 Different approaches have been applied to investigate, evaluate, and improve the nutrient use efficiency (NUE) of plants, depending on the purpose to which the concept has been put. Various approaches differ greatly in the scale and target for NUE investigation, evaluation, and improvement: (1) the investigation of the physiology and genetics of NUE at single tissue, cell and molecular scales, targeting understanding and improvement of NUE-related traits through, for example, plant breeding; and (2) the assessment of NUE and nutrient balances at whole plant, field, and regional scales for evaluating crops with NUE-improved traits (Table 2.1). In this context, simulation modeling and life cycle assessments (LCAs) are also used for NUE evaluation and are promising especially for bridging the various scales. A complication is that NUE alone, irrespective of the scale at which it is assessed, often is inadequate for addressing all the relevant aspects, for example, in an agricultural sustainability context. For example, the consideration of crop rotations could be an efficient way to improve the NUE at farm scale, but is difficult to accommodate in the NUE approaches that Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00002-8 Copyright © 2017 Elsevier Inc. All rights reserved.

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Table 2.1  Overview of the Various Approaches Used to Improve and Evaluate the Nutrient use Efficiency (NUE) of Crops From Molecular to Regional Scale Approaches for NUE Improvement or Evaluation

Scales

Targets

Investigations of the physiology behind NUE9–13

Single-tissue phenotype, single cell

Evaluation (physiology) and improvement (plant breeding)

Identification of genetic background for NUE-related traits14–18

Tissue or whole-plant phenotype, genotype, molecular

Evaluation (genetics) and improvement (plant breeding)

Concepts for whole-plant NUE assessment19–27

Whole-plant phenotype to field

Evaluation (physiology and ecology)

Crop NUE assessment using “difference methods”28

Field to farm

Evaluation (cropping system and farm)

Nutrient balances29,30

Farm to region

Evaluation (e.g., environmental issues)

Modeling, lifecycle assessments31–34

All scales, can integrate various scales

Evaluation at higher scale of process acting at lower scale

have been developed for the lower scales. Approaches to improve nutrient uptake mechanisms, for example, N fixation at tissue, cell, and molecular scales have to be validated at the higher scales to proof to be relevant also in practical crop culture at field and farm levels, because for instance increased root nutrient uptake rate assessed in laboratory conditions not necessarily translates into greater overall crop NUE in field conditions.6 In addition, higher NUE could be achieved simply by sacrificing crop yields, but this would not be economically sustainable or viable for the environment.7 In this chapter, various approaches and conceptions for assessing NUE across different scales are discussed with particular focus on the upscaling from molecular and cell levels, at which many of the mechanistic approaches for improving crop NUE are investigated, to the higher scales. N and to some extend P will be the most prominent nutrients in the discussions, as these elements frequently are the most limiting nutrients in crop production2,3; and there is evidence that the plant N concentration pattern often closely reflects the corresponding pattern seen also for other nutrients.8

PHYSIOLOGY AND GENETICS OF NUTRIENT USE EFFICIENCY ROOT DEVELOPMENT IN RESPONSE TO NUTRIENT AVAILABILITY The macronutrients N and P are required in core cellular processes and their availability therefore often limits plant growth. Plants adjust growth and development in several ways to improve the uptake and use of nutrients so as to ultimately maximize reproductive success (e.g., in annuals) and/or survival into a following growing season (i.e., perennials). Overall NUE is the outcome of the complex interaction between various single plant traits, integrating environmental, and internal signals over time and ­accommodating the history of plant growth and development, as well as metabolic processes. In response to low N and P availability, root growth is prioritized over shoot growth to increase root system size35 and root system architecture is modified to penetrate the soil efficiently to increase

 Physiology and genetics of nutrient use efficiency

33

the whole plant nutrient uptake.36 In the form of nitrate, N is accessible and abundant at neutral pH and in aerobic soils, while ammonia-N is the more abundant N form under low oxygen, low pH, and in waterlogged conditions. In low-N conditions, the root system generally develops longer primary and lateral roots. At high nitrate availability, a nitrate transporter located in the roots acts as a sensor for nitrate and mediates interactions with auxin; this leads to the increased formation and extension of l­ ateral roots and thereby improved foraging for the available nitrate.36 The high availability of ammonium causes stunted roots and toxicity symptoms. Compared to N, P is often less accessible in soils as it is insoluble at both low and high pH. Interestingly, the morphological responses to low P availability share some similarities to the responses to high nitrate availability, including the increased formation of lateral roots and root hairs.37 Some plants, for examples, lupins (species of the genus Lupinus L.), seabuckthorn (species of the genus Hippophae L.), and species in the Proteaceae and Cyperaceae families, form clusters of closely spaced short lateral rootlets that particularly improve the exploration and uptake of P.38,39 Also, the formation of aerenchyma by maize (Zea mays L.) roots grown in low-P conditions is an efficient strategy to explore P especially in such conditions.40 Soil exploration and uptake by the roots is essential to meet the demand for nutrients by the rest of the plant, and thus for overall NUE of the plants. Thus, root growth, root system architecture, and nutrient uptake rates are major factors influencing plant nutrient acquisition and NUE; and knowledge about the genetic control mechanisms of these traits is essential in plant breeding for improved NUE.

ROOT INTERACTIONS WITH MICROORGANISMS UNDER LOW NUTRIENT AVAILABILITY Roots interact with microbial communities and host microbial symbionts, such as mycorrhiza.41,42 These interactions can play multiple beneficial roles for plants, of which one is to improve the nutrient uptake and thereby plant growth and crop production.42,43 The root system of crop legumes forms symbiotic root nodules harboring N2-fixing rhizobium bacteria. The N2-fixation process is largely selfregulated and affected by the N sink strength of the plant; therefore the availability of N is a seemingly smaller problem for most legume crops compared to other crops.44 From a NUE perspective, the efficiency by which N2 is fixed depends to a great extent on the strain of rhizobium and its compatibility with the plant.44 This aspect has received significant attention and fruitful improvements of inoculants have been made, however, as agriculture is diversified with new legumes, new inoculants will be needed continuously. Due to the capacity of N fixation, the main focus of NUE investigations in legumes is often on nutrients other than N. For example, legumes require high amounts of P, possibly due to the high metabolic rate of the root nodules.45 As the primary function of legume roots is not N acquisition, their root morphology and physiology is often more adapted to low-P conditions.37 When considering NUE for improving crop yields, the root interactions with microorganisms, such as mycorrhiza and Nfixing rhizobia might be important issues especially in the crops forming these interactions and when the crops are grown in low nutrient-input agriculture.3,43

METABOLISM AND GENE REGULATION The NUE depends on the nutrient uptake, transport, assimilation, storage, remobilization, and synthesis of storage compounds during plant growth and development. Knowledge about these processes and the mechanisms regulating them is obtained through studies of metabolism, gene expression, and gene sequence analyses.

34

CHAPTER 2  ROLE OF NUTRIENT-EFFICIENT PLANTS

In the forms of ammonium, nitrate, urea or amino acids, N is assimilated into the amino acid metabolism.9,11 While ammonium is immediately assimilated into amino acids after uptake, nitrate is reduced and assimilated in roots or transported to the shoot for reduction and assimilation. Nitrate can also be stored in these organs. Nitrate is first reduced by nitrate reductase to nitrite; which is then further reduced by nitrite reductase to ammonium. These enzymatic steps are regulated at the transcriptional and enzyme levels in response to nitrate availability. To acquire the nitrate, ­several nitrate transport proteins act in roots, as well as throughout the plant. The nitrate transporters are encoded by many genes each with enhanced expression in different cells of the roots, such as epidermis, cortex, and phloem companion cells; the localization within the cells are the plasma and vacuole membranes.9,10 In cereals, for example, rice (species of the genus Oryza L.), wheat (species of the genus Triticum L.), and maize, the gene families nitrate transporter 2 and 3 (NRT2 and NRT3), and nitrate/peptide transporter (formerly partly NRT1) have been identified, encoding transport proteins involved in the high- and low-affinity transport systems.9,10,46–48 Recently, the nitrate transporter OsNRT2.3, which is part of the high-affinity nitrate uptake system, was overexpressed in rice and the performance of the plants was studied under field conditions; the splice form OsNRT2.3b resulted in more rapid adjustments to changed N supply and improved growth, grain yield, and NUE.49 Ammonium is an abundant form of N in anaerobic waterlogged soils and has received most attention in studies of rice, but is also relevant for temperate crops under conditions of waterlogging. Ammonium uptake transporters are encoded by two gene families expressed in roots.48 Ammonium, either taken up directly from the soil or obtained from nitrate reduction, is assimilated via the glutamine synthetase (GS)/glutamine-2-oxoglutarate aminotransferase (GOGAT) cycle. In addition to the primary N assimilation, ammonia generated in large quantities via photorespiration in photosynthetic tissues and via protein turnover during senescence or seed germination is also assimilated through the GS/GOGAT cycle.50 The chloroplastic GS isoform (i.e., GS2) is essential for survival under photorespiratory conditions. The isoform GS1 is encoded by several genes in a gene family, each with distinct expression patterns and affinities for ammonium and glutamate suggesting that they are important in different processes. In cereals, GS1 isoenzymes are also important for plant development and seed yield.51,52 Overexpression of GS resulted in increased biomass in wheat and rice.53,54 In rice, the overexpression of NADH–GOGAT, which takes part in ammonium assimilation in the root, resulted in 80% higher seed yield.55 The further biosynthesis of amino acids involves the enzymes aspartate aminotransferase and asparagine synthase, forming asparagine as a transport form of N. The increased expression of aspartate aminotransferase affected seed protein content and also the activity of phosphoenolpyruvate carboxylase.13 In legume crops, the major part of N is commonly obtained from the N2-fixing root nodules and converted into amino acids in the root nodules; the N is then transported in the form of amino acids (e.g., asparagine) and ureides (e.g., allantoin and allantoic acid) to the sink tissues. This is a major difference to cereal crops, in which N is usually transported as nitrate. To address the potential roles of N transport processes in legumes (but not cereals), the expression of amino acid transporters may therefore be considered.56 Another aminotransferase is the alanine aminotransferase, which is important during the recovery from stress and hypoxia. Interestingly, its overexpression in rape (Brassica napus L.) resulted in increased biomass production and seed yield under N-limiting conditions; and plants over-expressing AlaAT maintained yield even with 40% lower N application rate compared to the conventional cultivation practice.12,57

 Physiology and genetics of nutrient use efficiency

35

Several genes controlling regulatory networks involved in N metabolism have been investigated as possible targets for improving NUE. For example, the overexpression of the nitrate-inducible transcription factor TaNAC2-5A resulted in enhanced nitrate influx rate, higher N accumulation in the shoot and higher grain yield in wheat.58 The increased expression of the transcription factor ZmDof1 in transgenic rice induced the expression of PEPC genes, modulated C and N metabolites, increased N assimilation, and enhanced growth under low-N conditions.59 Recently, the Bric-a-Brac/Tramtrack/Broad (BT) gene family was discovered as a negative regulator of nitrate transporter genes and NUE in rockcress (species of the genus Arabidopsis Heynh.) and rice, and was suggested as a target for future research to improve NUE in crops.60 An additional promising candidate for further research is the transcription factor TaNFY that responds to both N and P starvation.61 In response to P, plant roots secrete acid phosphatases and organic acids that solubilize P in soil, and express phosphate transporter genes. The low P response is regulated by a network of control genes including a transcription factor that acts as a major P starvation response regulator (i.e., PHR1), and genes involved in P homeostasis.62 The overexpression of PHR1 in wheat has been shown to result in the upregulation of P starvation response genes, stimulation of lateral root branching and improved P uptake.63

REMOBILIZATION OF NUTRIENTS IN THE CROP PLANT LIFE CYCLE Plant seeds often contain the adequate nutrient mix for rapid growth during the first (and often critical) period of time after seed germination.64 Thus, the nutrients needed during seedling establishment and early growth are transferred from senescing plant parts to the seeds or other perennial plant parts during the process of remobilization; and nutrient remobilization therefore affects the NUE.65 For example, in cereals, the remobilization of N in proteins during leaf senescence and grain filling is essential for transferring N to the seed,11 and the effective translocation of N to the grain during grain filling has been identified as a candidate trait for improving the NUE in wheat.66 Also, some genes involved in leaf senescence, the associated grain filling and quality aspects have been identified for wheat.67,68 For example, regarding quality, an increased N content as protein is desired in food crops, while a high P content is not.69 These aspects of seed nutrient quality affect NUE and can be manipulated by plant breeding.

FINDING GENES FOR NUTRIENT USE EFFICIENCY For identification of genes controlling traits associated with NUE numerous studies have started from the phenotypic variation on the population level rather than knowledge about gene function. These studies have either involved mapping populations derived from crosses between parental genotypes, that is, quantitative trait loci (QTL) mapping, or natural populations, or panels of plants (association mapping) differing in traits associated with NUE. A large number of QTL, each with small to large ­effects on the phenotype, have been reported for different crops.14 Of special interest are QTL located in the same genomic region in mapping populations grown in different environmental conditions and years, for example, in a QTL mapping study with barley (species of the genus Hordeum L.).15 Some QTL for NUE in cereals have been mapped close to the genomic positions of structural genes, such as GS and NADH–GOGAT.16–18 Moreover, colocated QTL for root system morphology and different NUE traits have been found in cereals and legumes, which in most

36

CHAPTER 2  ROLE OF NUTRIENT-EFFICIENT PLANTS

cases were cultivated under low P70; while only two QTL studies involving maize and wheat have been conducted under N variability.71,72 Potentially important QTL for NUE should be further investigated and validated in different environments and genetic backgrounds using different mapping populations including near-isogenic lines or similar. Fine mapping and map-based cloning of the validated QTL may be used to further constrain the genomic region with the actual NUE-related gene(s). The search for these genes will be facilitated for crop species with complete genome sequences available.

FUTURE NUTRIENT-EFFICIENT CROPS More nutrient-efficient crops are important in future agriculture. Crop traits to be targeted include those enhancing nutrient uptake, transport, and remobilization; and the improved traits should be evaluated across all scales, that is, from molecular through individual plant, stand farm, and regional scale. Management aspects and the search for efficient beneficial symbioses should be a part of the strategy. To obtain nutrient-efficient plants, the so far identified physiological processes and genes are promising. Also, further crop breeding should make use of the knowledge and genetic resources for prebreeding that could be obtained from various plant species and the wild relatives of crops adapted to low-nutrient environments. Moreover, due to the loss of genetic and phenotypic diversity during crop domestication and breeding, the wild crop relatives make up an important gene pool for the search and introgression of novel genes and alleles for NUE-related traits. Differences in life history strategies ranging from short-lived annuals to long-lived perennials, as well as the consideration of changing nutrient demands during crop development may provide further knowledge for development of nutrient-efficient crops. The modern genetic tools, such as genome-wide association studies will facilitate the search. The development of high-yielding and nutrient-efficient cultivars adapted to farming under low fertilizer input will be an important component in an integrated global effort to reach sustainable farming and food security—a new green revolution.

ASSESSMENT AND EVALUATION OF NUTRIENT USE EFFICIENCY The previous section briefly reviewed the different physiological and genetic routes for improving crop NUE and the success of any crop improvement actions should be assessed with appropriate tools. Various approaches have been proposed and used to assess the NUE of plants, but the different approaches are often not compatible and the research performed is difficult to integrate due to lack of a common basis.6,73 We here briefly review some of the most popular approaches used in different contexts and scales. In general, NUE often considers the processes of carbon gain and loss in relation to the processes associated with the gain and loss of the major growth-limiting nutrients, and is often expressed in the form of mass balances between the input of nutrients and the output of some kind of biomass (e.g., whole-plant net biomass accumulation or harvested biomass yield). N and P are the nutrients that most frequently limit plant growth, although the growth conditions (e.g., temperature) and other resources (e.g., water) might often greatly influence the nutrient limitation. In the crop production context, NUE conceptions usually focus on the specific biomass fractions forming yield, which are defined by the harvested product and its quality (e.g., protein content); and the nutrients that are available to produce the biomass (e.g., soil nutrients and nutrient fertilizers). In ecological contexts, NUE conceptions

 Assessment and evaluation of nutrient use efficiency

37

c­ onsider the whole-plant biomass production in relation to plant internal nutrient accumulation and/or biomass and nutrient losses at the ecosystem level. Nutrient balances and budgets; and the associated NUE conceptions focus on nutrient output/input rations and are often used to quantify the amounts of nutrients imported and exported from a system; these nutrient balances can be calculated at any scale (e.g., farm, catchment, region).

ECOLOGICAL APPROACHES OF NUTRIENT USE EFFICIENCY One of the early NUE conceptions developed in ecology related, that is, the biomass losses to the ­nutrient losses at the ecosystem scale.74 Another attempt, developed at the individual plant scale, ­defined the NUE in terms of the plant biomass production in relation to plant-internal nutrient (especially N) contents; that is, the inverse of plant nutrient concentration.22 Later, the mechanistic relationships between the accumulation of the growth-limiting nutrients (mostly N) over time and biomass production were investigated by means of the nutrient productivity concept.20 Nutrient productivity20 was considered being one of the two main components of NUE, the other component being the nutrient carryover from annual to perennial plant parts, that is, the mean residence time of the nutrient in consideration.21,24 The mean residence time is here the expected length of time that a unit of nutrient newly taken up by the plant is retained before lost, and integrates the mean standing plant nutrient and the total nutrient uptake in a given time period.24 The conception can be defined both for steady- and nonsteady-state systems, and is then applicable for both perennial and annual stands.24 Analyses of the mechanistic background of the two components, that is, nutrient productivity and mean residence time of nutrient, resulted in a number of hypotheses regarding the functional relationships between nutrient productivity and nutrient conservation mechanisms in plants.25–27 Applications of these approaches have made valuable contributions to the functional understanding of plant nutrient use and growth, but the results in many cases are difficult to apply in a crop production context, mostly due to the lack of focus on the harvested products, and also because of the ecological conceptions that do not accommodate a nutrient uptake component clearly separated from the mean residence time. The quantification of nutrient uptake processes is however, central in the evaluation and improvement of NUE in a crop production context.

CROP PRODUCTION–RELATED APPROACHES OF NUTRIENT USE EFFICIENCY The different approaches for assessing NUE in an agricultural context have been reviewed ­elsewhere.6,28,73,75 Point of departure is often that many crops are fertilized with large amounts of ­nutrient fertilizer, but only a small fraction of this fertilizer (roughly 5%–50%)73 is taken up by the plants. For the case of N, applied nutrients not taken up by the crop or immobilized in the soil by microorganisms are lost by volatilization, denitrification, leaching, and runoff, and can cause serious environmental problems.76 The nutrient losses can be reduced by enhancing the plant’s nutrient uptake efficiency, which is an important component in many conceptions for the assessment of NUE in a crop production context. Other possible routes for improving the overall NUE of a crop are modifying the efficiencies in plant-internal nutrient use (e.g., more yield per unit of nutrient taken up by the plant) and nutrient translocation from the nonharvested into the harvested plant parts.77 The NUE approaches used in agronomy are often some kind of mass balance ratios between crop yield and total plant nutrient content at final harvest and performed at field or plot scale. Some of the NUE

38

CHAPTER 2  ROLE OF NUTRIENT-EFFICIENT PLANTS

indices are calculated based on differences between the corresponding values in fertilized and unfertilized plots, that is, the difference methods, such as agronomic efficiency, crop recovery efficiency, and physiological efficiency.28 Time scale of most conceptions is one cropping season, which causes difficulties when the NUE of annual and perennial crops should be compared. In addition, the above approaches consider only the nutrient and biomass quantities in the final crop harvest, which can be considered as the outcome of the growth and development processes occurring over a longer period in which the nutrient in consideration may not always be the most limiting factor for growth. To address some of the aforementioned issues, a flexible NUE conception was developed that considers aspects from crop sowing to harvested product, and can be applied for both perennial and annual crops.23,78 The NUE components address similar processes compared to other approaches,19 that is, nutrient uptake and utilization efficiency (similar to nutrient productivity20); but accommodate an additional component recognizing the importance of nutrient conservation for crop growth and quality development, that is, the nutrient concentration in the harvested product.23 In this approach, the overall NUE reflects the final output (or accumulation) of the nutrient required to produce the yield, in relation to its input through the plant-internal resource storage, that is, in seeds; and thus describes the plant’s ability to multiply the nutrients available in seeds by means of the three NUE components. Environmental factors, such as nutrient fertilization are considered to affect the NUE and its components, but are not intrinsic parts of the calculations. The clear separation of plant characteristics determining the NUE and the environmental factors affecting it facilitates identification of desirable crop traits for improved NUE, for example, by variety selection and plant breeding. With a focus on N, the various conceptions for the assessment of NUE in a crop production perspective are here illustrated by quantifying the different NUE indices and components based on a data set from a field experiment in which six spring wheat varieties were grown in moderately fertilized and unfertilized plots in Central Sweden79 (Table 2.2). In addition to the calculations using the original data, we also considered a future improved crop with enhanced N uptake efficiency by 50% at unchanged nutrient supply rate (Nup + 50, Table 2.2), a scenario which appears realistic in light of the current developments as the engineering of crops with increased N uptake efficiency is now considered a viable option.12 In the calculation of the Nup + 50 values, increase in grain yield was assumed to be proportional to the increase in plant-internal N, that is, the yield per mean plant N content and the grain N concentration were assumed to be unchanged by the improved N uptake efficiency. This assumption was made for simplicity, although the original data79 indicate that there could exist trade-offs between some of these traits, for example, improved N uptake efficiency is possibly associated with decreased yield per mean plant N content. Assessment of the N uptake efficiency by the approach of Moll et al.19 requires estimates of soil N contents as an input. Soil N estimates generally are very much linked to the soil analysis method being used,80 which also makes the calculation of N uptake efficiency; according to Moll et al.19 sensitive for the methodology of soil N analysis. The N uptake efficiency in the conception by Weih et al.23 does not require soil N analyses as an input, but instead is based on plant N analysis data from several developmental stages between seed grain and grain harvest. Both approaches give estimates of the crop N uptake at the whole-plant to field scales, but reflect different aspects of N uptake. For example, the N uptake efficiency according to Moll et al.19 is similar in the two fertilization levels of the reference scenario (Table 2.2), indicating that N uptake per unit N available as soil mineral and fertilizer N was here similar in both nutrient treatments. In contrast, the N uptake efficiency by Weih et al.23 is greatly increased with the added nutrient fertilizer, reflecting that the rate of N accumulation in the crop was greater in the fertilized plots.

 Assessment and evaluation of nutrient use efficiency

39

Table 2.2  Comparison of Various NUE Indices and Components Using Original Data From a Field Experiment in Which Six Varieties of Spring Wheat Were Grown at Two Nutrient Fertilization Levels (F0, no fertilization; and F+, fertilization with 81 kg N ha−1 Given as Ammonium Nitrate Mixed With Calcium Carbonate and Sulfur) in Central Sweden for One Growing Season79 Reference Scenario

Nup + 50 Scenario

Trait, NUE Index or Component

F0

F+

F0

F+

Y (kg ha )

2660

3840

3990

5760

56

104

84

156

N uptake efficiency (Moll et al.) (g g )

0.59

0.59

0.88

0.89

Nf/Nsoil

N utilization efficiency19 (g g−1)

48

37

48

37

Y/Nf

28

22

42

33

Y/Nsoil

N uptake efficiency (Weih et al.) (g g )

7.5

13.9

11.2

20.8

N’/Ns

Grain-specific N efficiency23 (g g−1)

86

65

86

65

Y/N’

Grain N concentration (g g )

−1

Nf (kg ha ) −1

19

19

−1

N use efficiency (Moll et al.) (g g ) −1

23

−1

Calculations

0.018

0.020

0.018

0.020

N yield/Y

−1

N use efficiency (Weih et al.) (g g )

11.5

18.1

17.3

27.2

N yield/Ns

Partial factor productivity28 (kg kg−1)

N/A

47

N/A

71

Y/fertilized N

−1

23

28

N/A

15

N/A

22

(YF+ − YF0)/fertilized N

−1

Crop recovery efficiency (kg kg )

N/A

0.6

N/A

0.9

(NfF+ − NfF0)/fertilized N

Physiological efficiency28 (kg kg−1)

N/A

25

N/A

25

(YF+ − YF0)/(NfF+ −NfF0)

Agronomic efficiency (kg kg ) −1

28

The reference scenario presents calculations based on the original data, and the Nup + 50 scenario presents the same data, but with the assumption that the N uptake efficiency is increased by 50%. Further explanations are given in the text. Y, Grain yield (biomass); Nf, final crop N content (subscripts F0 and F+ specify yields at different fertilization level used in the difference methods); Ns, N content in seed grain; Ni, crop N content at start of the main growing season; N’, crop N content during main growth period, that is, (Ni + Nf)/2; Nsoil, soil N content (here 95 kg ha−1 mineral N).79

The N utilization efficiency19 and grain-specific N efficiency23 are conceptually similar and ­generally reflect the capacity of a crop to generate yield from the N accumulated (also similar to the N productivity20,24). In the fertilized plots of the case study (Table 2.2) both indices decrease to approximately 75% of the corresponding values in the unfertilized plots; and the generally greater values of grain-specific N efficiency are caused by the different denominator in the two ratios. The N utilization efficiency19 assumes the final plant N content (at yield harvest) to be representative for the plant N over the whole growing season; whereas the mean plant N content over the main growing season is used as the denominator in the grain-specific N efficiency.23 The overall NUE by Moll et al.19 is the product of N uptake efficiency and N utilization efficiency, and reflects the yield output in relation to the (measured) soil N input; this NUE measure is sensitive for the methodology of soil N analysis in the same way as it was discussed for the N uptake efficiency in this approach. The NUE sensu Weih et al.23 is the product of N uptake efficiency, grain-specific N efficiency and grain N concentration; it relates the final N yield output to the seed N input, and therefore indicates the N accumulation capacity from the seed to the harvested grain. Both approaches thus allow the decomposition

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CHAPTER 2  ROLE OF NUTRIENT-EFFICIENT PLANTS

into several different aspects of NUE and the approach by Weih et al.23 accommodates the N content of the yield as a separate component; whereasthe approach by Moll et al.19 accommodates N as an intrinsic part of the final crop N (Nf). In contrast to the above NUE conceptions,19,23 the NUE indices based on the difference method (i.e., partial factor productivity, agronomic efficiency, crop recovery efficiency, and physiological efficiency, Table 2.2) require data from both fertilized and unfertilized plots, and are very limited for ­comparisons of crop NUE across different environments and genotype by environment interactions. These methods are however, simple and costefficient, and therefore popular especially in on-farm research. The partial factor productivity becomes identical to the N use efficiency approach by Moll et al.19 when using the fertilizer N instead of the soil N content. Also, the physiological efficiency can be seen as an integration of the N utilization efficiency19 over two fertilization treatments (F0 and F+). Crop recovery and agronomic efficiency become similar to the N uptake and N utilization efficiency, respectively, by Moll et al.19 approach when using the fertilizer N instead of the soil N content, but the difference methods integrate over two fertilization treatments. The Nup + 50 scenario in Table 2.2 quantitatively simulates how a potential crop improvement in terms of increased N uptake efficiency by 50% (assumed is no effect on the other NUE components) would be reflected by the various NUE indices and components. As expected, such a modification would be reflected not only in the N uptake efficiency and the overall NUE,19,23 but also the majority of the NUE indices based on difference methods. An advantage of the more flexible conception integrating a greater number of NUE aspects23 is that it allows, at whole-plant to field scales, the integrated analysis of crop improvements affecting NUE (e.g., by selection and breeding) at a greater detail level compared to the simpler conceptions, that however, are more appropriate for assessing and evaluating the effects of crop improvements at the farm scale.

NUTRIENT BALANCES AND BUDGETS, MODELING, AND LIFE CYCLE ASSESSMENTS Nutrient balances and budgets are frequently used to evaluate the NUE at the agroecosystem scale and quantify (potential) nutrient turnover rates in agriculture by estimating input, storage, and output processes by mass balance.29,30 In these approaches, NUE is often defined as the ratio between nutrient flows out of a system by means of the removal of harvested products, and the nutrient flows into a system required to generate the harvested product.29 Thus, in contrast to most of the ecological and agricultural approaches for NUE assessment discussed earlier (Table 2.2), the NUE in nutrient balances and budgets usually refers to nutrient flow fractions rather than ratios between biomass yields and nutrients, and adopts large-scale system boundaries, such as farm or land surface.29 Conceptually, the NUE approach by Weih et al.23 shows similarities to the NUE indices used in nutrient budgets/balances, as it defines the NUE in terms of a nutrient flow fraction in which the output is the nutrient yield of the crop and the input is the seed nutrient content; the system boundary is here an individual plant or crop observed during its entire life cycle. The nutrient flow fraction represented by the overall NUE23 is decomposed into a shortterm nutrient balance (i.e., N uptake efficiency) and two biomass-to-nutrient ratios (i.e., grain-specific N efficiency and grain N concentration, Table 2.2), which represent functionally important identities in a crop improvement context. Nutrient budgets/balances and the corresponding NUE indices, usually assessed at farm or ecosystem scales, are not easily applicable in the evaluation of crop improvement strategies acting at individual plant (or lower) scales, as the nutrient budgets/balances do not provide a direct link to the functional identities important in crop improvement, that is, the plant functional traits.81 However, the results from nutrient budgets/balances can be applied in agroecosystem simulation

REFERENCES

41

modeling and LCA either to identify the bottlenecks for NUE improvement at farm and regional scale,31 or to quantify at regional scale, the potential environmental impact of using crops with improved traits enhancing NUE.32,33 For example, the environmental footprint of barley with anticipated increased N uptake efficiency by 50% (similar to the Nup + 50 scenario in Table 2.2) and by 100% was assessed for two regions in Sweden by using LCA, and the improved N uptake efficiency was here predicted to affect the eutrophication and global warming potential.34 Thus, using LCA and other simulation models, crop improvements of NUE-related traits performed at the molecular scale12 can be linked to their likely effects at higher scales (e.g., regions), when no long-term experimental data are available. However, advanced crop models are required to better reflect the effect of plant breeding actions targeting NUE-related traits on the expected yield at field to regional scales. Future model development should involve expertise in plant breeding, plant physiology, and dynamic crop and soil modeling, thus better enabling the bridging of molecular, individual-plant, and regional scales in NUE research and development.

CONCLUSIONS More nutrient-efficient plants are required for increasing and/or maintaining yields, and at the same time reducing the negative environmental impacts of crop production. Since Nfrequently is l­imiting crop growth and studies of modified expression of genes important for N uptake, assimilation, ­transport, and retranslocation have been demonstrated to affect plant growth, further exploration of N uptake and transport mechanisms is relevant for improving crop NUE. Several promising candidate genes are available and should be studied together with proper phenotyping of NUE-related traits and final verification under field conditions. The evaluation of crop improvements enhancing NUE can be done by using various methodologies, depending on the scale at which the assessments should be evaluated and the specific traits in consideration. Appropriate methodologies are available to link the processes acting at molecular, individual plant, farm, and regional scales, but require increased collaborative efforts between molecular biologists, plant ecologists, agronomists, and ecosystem modelers.

REFERENCES 1. Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S. Agricultural sustainability and intensive production practices. Nature 2002;418(6898):671–7. 2. Fageria NK, Baligar VC, Li YC. The role of nutrient efficient plants in improving crop yields in the twenty first century. J Plant Nutr 2008;31:1121–57. 3. Spiertz JHJ, Ewert F. Crop production and resource use to meet the growing demand for food, feed and fuel: opportunities and constraints. Njas-Wagen J Life Sci 2009;56:281–300. 4. Norton R, Davidson E, Roberts T. Nitrogen use efficiency and nutrient performance indicators. Global Partnership on Nutrient Management Task Team Workshop. Washington, DC; 2015. p. 1–14. 5. Mengel K, Kirkby EA. Principles of plant nutrition. Dordrecht: Kluwer Academic Publishers; 2001. 6. Reich M, Aghajanzadeh T, De Kok LJ. Physiological basis of plant nutrient use efficiency: concepts, opportunities and challenges for its improvement. In: Hawkesford MJ, Kopriva S, DeKok LJ, editors. Nutrient use efficiency in plants: concepts and approaches. Dordrecht: Springer; 2014. p. 1–27. 7. Dibb DW. The mysteries (myths) of nutrient use efficiency. Better Crops 2000;84:3–5. 8. Weih M, Pourazari F, Vico G. Nutrient stoichiometry in winter wheat: element concentration pattern reflects developmental stage and weather. Sci Rep 2016;6:35958.

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9. Krapp A. Plant nitrogen assimilation and its regulation: a complex puzzle with missing pieces. Curr Opin Plant Biol 2015;25:115–22. 10. Wang Y-Y, Hsu P-K, Tsay Y-F. Uptake, allocation and signaling of nitrate. Trends Plant Sci 2012; 17(8):458–67. 11. Xu G, Fan X, Miller AJ. Plant nitrogen assimilation and use efficiency. Ann Rev Plant Biol 2012;63(1):153–82. 12. McAllister CH, Beatty PH, Good AG. Engineering nitrogen use efficient crop plants: the current status. Plant Biotechnol J 2012;10(9):1011–25. 13. Good AG, Shrawat AK, Muench DG. Can less yield more? Is reducing nutrient input into the environment compatible with maintaining crop production? Trends Plant Sci 2004;9:597–605. 14. Han M, Okamoto M, Beatty PH, Rothstein SJ, Good AG. The genetics of nitrogen use efficiency in crop plants. Ann Rev Genet 2015;49(1):269–89. 15. Kindu G, Tang J, Yin X, Struik P. Quantitative trait locus analysis of nitrogen use efficiency in barley (Hordeum vulgare L.). Euphytica 2014;199:207–21. 16. Quraishi U, Abrouk M, Murat F, Pont C, Foucrier S. Cross-genome map based dissection of a nitrogen use efficiency ortho-meta QTL in bread wheat unravels concerted cereal genome evolution. Plant J 2011;65:745–56. 17. Liu ZY, Zhu CS, Jiang Y, et al. Association mapping and genetic dissection of nitrogen use efficiency-related traits in rice (Oryza sativa L.). Funct Integr Genom 2016;16(3):323–33. 18. Obara M, Kajiura M, Fukuta Y, Yano M, Hayashi M. Mapping of QTLs associated with cytosolic glutamine synthetase and NADH-glutamate synthase in rice (Oryza sativa L.). J Exp Bot 2001;52:1209–17. 19. Moll RH, Kamprath EJ, Jackson WA. Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization. Agron J 1982;74:562–4. 20. Agren GI. Theory for growth of plants derived from the nitrogen productivity concept. Physiol Plant 1985;64:17–28. 21. Berendse F, Aerts R. Nitrogen-use-efficiency: a biologically meaningful definition? Funct Ecol 1987;1:293–6. 22. Chapin III FS. The mineral nutrition of wild plants. Ann Rev Ecol Evol Syst 1980;11:233–60. 23. Weih M, Asplund L, Bergkvist G. Assessment of nutrient use in annual and perennial crops: a functional concept for analyzing nitrogen use efficiency. Plant Soil 2011;339:513–20. 24. Hirose T. Nitrogen use efficiency revisited. Oecologia 2011;166(4):863–7. 25. Ogawa T, Oikawa S, Hirose T. Nitrogen-utilization efficiency in rice: an analysis at leaf, shoot, and wholeplant level. Plant Soil 2016;404:321–44. 26. Eckstein RL, Karlsson PS, Weih M. Leaf life span and nutrient resorption as determinants of plant nutrient conservation in temperate-arctic regions. New Phytol 1999;143(1):177–89. 27. Garnier E, Aronson J. Nitrogen-use efficiency from leaf to stand level: clarifying the concept. In: Lambers H, Poorter H, VanVuuren MMI, editors. Inherent variation in plant growth. Physiological mechanisms and ecological consequences. Leiden: Backhuys Publishers; 1998. p. 515–38. 28. Dobermann AR. Nitrogen use efficiency: state of the art. US: Agronomy & Horticulture Faculty Publications; 2005. 29. Leip A, Britz W, Weiss F, de Vries W. Farm, land, and soil nitrogen budgets for agriculture in Europe calculated with CAPRI. Environ Pollut 2011;159(11):3243–53. 30. Oenema O, Kros H, de Vries W. Approaches and uncertainties in nutrient budgets: implications for nutrient management and environmental policies. Eur J Agron 2003;20(1–2):3–16. 31. Ahrens TD, Lobell DB, Ortiz-Monasterio JI, Li Y, Matson PA. Narrowing the agronomic yield gap with improved nitrogen use efficiency: a modeling approach. Ecol Appl 2010;20:91–100. 32. Strange A, Park J, Bennett R, Phipps R. The use of life-cycle assessment to evaluate the environmental impacts of growing genetically modified, nitrogen use-efficient canola. Plant Biotechnol J 2008;6(4): 337–45. 33. McDevitt JE, Mila i Canals L. Can life cycle assessment be used to evaluate plant breeding objectives to improve supply chain sustainability? A worked example using porridge oats from the UK. Int J Agric Sustain 2011;9(4):484–94.

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34. Tidaker P, Bergkvist G, Bolinder M, et al. Estimating the environmental footprint of barley with improved nitrogen uptake efficiency—a Swedish scenario study. Euro J Agro 2016;80:45–54. 35. Ericsson T. Growth and shoot:root ratio of seedlings in relation to nutrient availability. Plant Soil 1995;169: 205–14. 36. Forde BG. Nitrogen signalling pathways shaping root system architecture: an update. Curr Opin Plant Biol 2014;21:30–6. 37. Liu H, Tang C, Li C. The effects of nitrogen form on root morphological and physiological adaptations of maize, white lupin and faba bean under phosphorus deficiency. AoB Plants 2016;8. 38. Lambers H, Shane M, Cramer M, Pearse S, Veneklaas E. Root structure and functioning for efficient acquisition of phosphorus: matching morphological and physiological traits. Ann Bot 2006;98:693–713. 39. Shah SRU, Agback P, Lundquist P-O. Root morphology and cluster root formation by seabuckthorn (Hippophaë rhamnoides L.) in response to nitrogen, phosphorus and iron deficiency. Plant Soil 2015;397(1):75–91. 40. Lynch JP. Root phenes that reduce the metabolic costs of soil exploration: opportunities for 21st century agriculture. Plant Cell Environ 2015;38(9):1775–84. 41. Bharadwaj DP, Lundquist P-O, Alström S. Arbuscular mycorrhizal fungal spore-associated bacteria affect mycorrhizal colonization, plant growth and potato pathogens. Soil Biol Biochem 2008;40(10):2494–501. 42. Smith SE, Read DJ. Mycorrhizal symbiosis. 3rd edn. New York: Academic Press; 2008. 43. Rooney DC, Killham K, Bending GD, Baggs E, Weih M, Hodge A. Mycorrhizas and biomass crops: opportunities for future sustainable development. Trends Plant Sci 2009;14(10):542–9. 44. Zahran HH. Rhizobium-legume symbiosis and nitrogen fixation under severe conditions and in an arid climate. Microbiol Mol Biol Rev 1999;63(4):968–89. 45. Sulieman S, Tran L-SP. Phosphorus homeostasis in legume nodules as an adaptive strategy to phosphorus deficiency. Plant Sci 2015;239:36–43. 46. Léran S, Varala K, Boyer J-C, et al. A unified nomenclature of nitrate transporter 1/peptide transporter family members in plants. Trends Plant Sci 2013;19(1):5–9. 47. Plett D, Toubia J, Garnett T, Tester M, Kaiser B. Dichotomy in the NRT gene families of dicots and grass species. PLoS One 2010;5:e15289. 48. von Wittgenstein NJ, Le CH, Hawkins BJ, Ehlting J. Evolutionary classification of ammonium, nitrate, and peptide transporters in land plants. BMC Evol Biol 2014;14:11. 49. Fan X, Tang Z, Tan Y, et al. Overexpression of a pH-sensitive nitrate transporter in rice increases crop yields. Proc Natl Acad Sci 2016;113(26):7118–23. 50. Lam H, Coschigano K, Oliveira I, Melo-Oliveira R, Coruzzi G. The molecular-genetics of nitrogen assimilation into amino acids in higher plants. Ann Rev Plant Physiol Plant Mol Biol 1996;47:569–93. 51. Martin A, Lee J, Kichey T, Gerentes D, Zivy M. Two cytosolic glutamine synthetase isoforms of maize are specifically involved in the control of grain production. Plant Cell 2006;18:3252–74. 52. Tabuchi M, Sugiyama K, Ishiyama K, et al. Severe reduction in growth rate and grain filling of rice mutants lacking OsGS1;1, a cytosolic glutamine synthetase1;1. Plant J 2005;42(5):641–51. 53. Habash D, Massiah A, Rong H, Wallsgrove R, Leigh R. The role of cytosolic glutamine synthetase in wheat. Ann Appl Biol 2001;138:83–9. 54. Brauer E, Rochon A, Bi Y, Bozzo G, Rothstein S, Shelp B. Reappraisal of nitrogen use efficiency in rice overexpressing glutamine synthetase1. Physiol Plant 2011;141:361–72. 55. Tabuchi M, Abiko T, Yamaya T. Assimilation of ammonium ions and reutilization of nitrogen in rice (Oryza sativa L.). J Exp Bot 2007;58(9):2319–27. 56. Tegeder M. Transporters involved in source to sink partitioning of amino acids and ureides: opportunities for crop improvement. J Exp Bot 2014;65(7):1865–78. 57. Good AG, Johnson SJ, Pauw M, Carroll RT, Savidov N, Vidmar J. Engineering nitrogen use efficiency with alanine aminotransferase. Can J Bot 2007;85:252–62. 58. He X, Qu BY, Li WJ, et al. The nitrate-inducible NAC transcription factor TaNAC2-5A controls nitrate response and increases wheat yield. Plant Physiol 2015;169(3):1991–2005.

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59. Kurai T, Wakayama M, Abiko T, Yanagisawa S, Aoki N, Ohsugi R. Introduction of the ZmDof1 gene into rice ­enhances carbon and nitrogen assimilation under low-nitrogen conditions. Plant Biotechnol J 2011;9(8):826–37. 60. Araus V, Vidal EA, Puelma T, et al. Members of BTB gene family regulate negatively nitrate uptake and nitrogen use efficiency in Arabidopsis thaliana and Oryza sativa. Plant Physiol 2016;171:1523–32. 61. Qu B, He X, Wang J, et al. A wheat CCAAT box-binding transcription factor increases the grain yield of wheat with less fertilizer input. Plant Physiol 2015;167(2):411–23. 62. Yang XJ, Finnegan PM. Regulation of phosphate starvation responses in higher plants. Ann Bot 2010;105(4): 513–26. 63. Wang J, Sun J, Miao J, et al. A phosphate starvation response regulator Ta-PHR1 is involved in phosphate signalling and increases grain yield in wheat. Ann Bot 2013;111(6):1139–53. 64. Liptay A, Arevalo AE. Plant mineral accumulation, use and transport during the life cycle of plants: a review. Can J Plant Sci 2000;80:29–38. 65. Masclaux-Daubresse C, Reisdorf-Cren M, Orsel M. Leaf nitrogen remobilisation for plant development and grain filling. Plant Biol 2008;10:23–36. 66. Foulkes MJ, Hawkesford MJ, Barraclough PB, et al. Identifying traits to improve the nitrogen economy of wheat: recent advances and future prospects. Field Crops Res 2009;114:329–42. 67. Waters BM, Uauy C, Dubcovsky J, Grusak MA. Wheat (Triticum aestivum) NAM proteins regulate the translocation of iron, zinc, and nitrogen compounds from vegetative tissues to grain. J Exp Bot 2009;60:4263–74. 68. Asplund L, Bergkvist G, Leino MW, Westerbergh A, Weih M. Swedish spring wheat varieties with the rare high grain protein allele of NAM-B1 differ in leaf senescence and grain mineral content. PLoS One 2013;8. 69. Veneklaas EJ, Lambers H, Bragg J, et al. Opportunities for improving phosphorus-use efficiency in crop plants. New Phytol 2012;195(2):306–20. 70. Li XX, Zeng RS, Liao H. Improving crop nutrient efficiency through root architecture modifications. J Integr Plant Biol 2016;58(3):193–202. 71. Li P, Chen F, Cai H, et al. A genetic relationship between nitrogen use efficiency and seedling root traits in maize as revealed by QTL analysis. J Exp Bot 2015;66(11):3175–88. 72. Maccaferri M, El-Feki W, Nazemi G, et al. Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. J Exp Bot 2016;67(4):1161–78. 73. Carranca C. Nitrogen use efficiency by annual and perennial crops. Lichtfouse E, editor. Farming for food and water security, Dordrecht: Springer; 2012. p. 57–82. 74. Vitousek P. Nutrient cycling and nutrient use efficiency. Am Nat 1982;119:553–72. 75. Cassman KG, Dobermann A, Walters DT. Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio 2002;31:132–40. 76. Canfield DE, Glazer AN, Falkowski PG. The evolution and future of earth’s nitrogen cycle. Science 2010;330(6001):192–6. 77. Dawson JC, Huggins DR, Jones SS. Characterizing nitrogen use efficiency in natural and agricultural ecosystems to improve the performance of cereal crops in low-input and organic agricultural systems. Field Crops Res 2008;107:89–101. 78. Weih M. A calculation tool for analyzing nitrogen use efficiency in annual and perennial crops. Agronomy 2014;4:470–7. 79. Asplund L, Bergkvist G, Weih M. Proof of concept: nitrogen use efficiency of contrasting spring wheat varieties grown in greenhouse and field. Plant Soil 2014;374:829–42. 80. Carter MR, Gregorich EG. Soil sampling methods of analysis. 2nd edn. Boca Raton, FL: Taylor & Francis; 2008. 81. Weih M, Hoeber S, Beyer F, Fransson P. Traits to ecosystems: the ecological sustainability challenge when developing future energy crops. Front Energy Res 2014;2:1–5.

CHAPTER

MACRONUTRIENT SENSING AND SIGNALING IN PLANTS

3 Christian Weissert, Julia Kehr

University of Hamburg, Hamburg, Germany

INTRODUCTION The plant kingdom has evolved into a large diversity of species, each of which have adapted to very different environments and varying local conditions, including seasonal biotic and abiotic changes. Soil nutrient availability differs dramatically according to soil type and is also dependent on nutrient solubility. Optimal growth conditions are only met under controlled conditions, such as in the greenhouse. In nature, meanwhile, plants usually show reduced growth due to limiting factors, such as nutrient deficiency. Plants require 17 different elements for optimal growth, 14 of which are taken up from the soil.1 The soil-derived nutrients are further classified into macro- and micronutrients, depending on the quantities required. The macronutrients include phosphorus (P), nitrogen (N), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S). Intensive farming has transformed many soils into very poor substrates requiring the application of fertilizers. The molecular mechanisms underlying the responses to nutrient limiting conditions have been investigated for decades in the hope of reducing the nutrient requirements of crops. In the following sections, the central responses and mechanisms by which plants sense and signal the limitation of the above-mentioned macronutrients are summarized and discussed.

PLANT MACRONUTRIENT STARVATION RESPONSES Plant responses to nutrient stress mostly involve increased root growth with parallel reduced shoot growth. This morphological response is accompanied by an increase in nutrient uptake mediated by the expression of high affinity transport systems. In the following paragraphs plant responses to macronutrient deficiency will be addressed.

PHOSPHORUS The P use efficiency of crops is usually relatively low (7 days stress conditions. In contrast, little is known about the events in early responses after 0–24 h. A study conducted by Zhang et al.145 shows that cucumber responds in a tissue-specific manner on the transcript level, and major changes in mRNA levels were detected in the phloem sap. Several hundred mobile mRNAs were identified for delivery to specific tissues via the phloem. The authors propose that the shoot vascular system perceives root-derived signals and that the phloem serves as the medium for sending these signals back to various sinks. Another phloem-mediated signal that has been proposed to play a role in Pi-deprivation is that of sucrose.146,147 Shoot-derived sucrose is translocated to the phloem and increases at the early stages of Pi-deficiency. Pi-deficiency activates sucrose-responsive genes, and the exogenous application of sugar induces many phosphate-inducible (PSI) genes. Removal of sucrose from the growth media was shown to impair the induction of these genes. A simplified model of the systemic signaling pathways in plants is depicted in Fig. 3.1.

NITROGEN Plants react to N availability in soil with local and systemic signaling events. Here, the central aspects of NO −3 -mediated signaling will be described. NO −3 itself was found to serve as a signaling molecule that alters gene expression within minutes, and is here designated as the primary NO −3 response (PNR).148,149 PNR triggers many physiological changes, including root development, leaf development, seed dormancy, and flowering time. At the molecular level signaling cascades of ion transport, nucleic acid biosynthesis, transcription, RNA processing, and hormone homeostasis are altered.150 As already mentioned above, the NO −3 transceptor NPF6.3 (NRT1.1) senses the presence of NO −3 . NPF6.3 activates phospholipase C that itself triggers an increase in cytoplasmic Ca2+ levels. The Ca2+ signal alters the expression of some primary response genes such as NRT2.1 and the transcription ­factor TGA1.151 However, not all NO −3 -responsive genes depend on this pathway, and multiple pathways downstream of the NPF6.3 transceptor may exist.90 In order to investigate the branching signaling pathways, varying approaches have been applied: phosphor proteomic analysis revealed 38 changes in the phosphorylation pattern of proteins in NO −3 -deprived seedlings.152 Most of the affected proteins were involved in fundamental metabolic pathways. With a similar approach Engelsberger and Schulze153 identified 589 differentially phosphorylated proteins after the addition of NO −3 to N-starved seedlings, including GPI anchored proteins, receptor kinases, TFs, and proteins involved in protein synthesis and degradation, and central and hormone metabolism. The two studies showed an overrepresentation of kinases, MAP kinases, sucrose nonfermenting related kinases, Ca-dependent kinases, and calcineurin-B-like interacting kinases (CIPKs). The studies further confirmed the role of CIPK8 and 23, both of which have a phosphorylating effect on the NO −3 tranceptor NPF6.3.91

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In order to understand the complex signaling pathways in response to NO −3 independent from NPF6.3 comes from numerous transcriptomic studies. In these studies, a series of transcription factors responding to NO −3 have been identified: NIN LIKE PROTEINS (NLP), TGACG MOTIF-BINDING FACTOR (TGA1), TGA4, ARABIDOPSIS NITRATE REGULATED 1 (ANR1), BASIC LEUCINE-ZIPPER 1 (bZIP1), LOB DOMAIN-CONTAINING PROTEIN (LBD37), LBD38, PCF (TCP)-DOMAIN FAMILY PROTEIN 20 (TCP20), NAC DOMAIN CONTAINING PROTEIN (NAC4), and SQUAMOSA PROMOTER BINDING PROTEINLIKE 9 (SPL9).154 Targets and roles have been described for NLP6 and 7, TGA1 and 4, bZip1, TCP20, LBD37 and 38, and SPL9. NLP6 was reported as a NO −3 responsive transcription factor that primarily responds to nitrate signals and activates genes involved in NO −3 assimilation and other regulatory genes including transcription factors.155 NPL7 is not transcriptionally regulated by NO −3 , but has been shown to be rapidly located to the nucleus when NO −3 is applied.156 First targets were identified using chromatin immunoprecipitation combined with DNA chip technology (ChIP-chip). 851 genes in total were ­observed to bind NLP7. The role in the primary response to NO −3 of NPL7 was further described in NLP7 KO plants: 101 genes were found to be differentially regulated in their involvement in N metabolism, NO −3 -related signaling, or hormone transport, and metabolism.156 TGA1 and 4, in contrast, are transcriptionally regulated by NO −3 and levels increased early after the application of NO −3 .157 Double KO plants were impaired in primary root length and lateral root density. Both were also regulated by Ca2+ levels, indicating a crosstalk in Ca2+ and PLC-mediated signaling.151 In microarray-based studies of the mutant identified targets of TGA1 and 4 results showed most were involved in primary NO −3 responses of N metabolism and NRT2.1 and NRT2.2.157 BZip1 has been described as playing a role for both light and NO −3 signaling.158 The TF responds to NO −3 and NH4NO3 by transiently binding to its target promoters.159 TCP20 was identified in a yeast two-hybrid screen.160 The TF is expressed in root tips, root vasculature, and young leaves and binds to type A response regulators 5 and 7, which are upregulated in the shoot under NO −3 deficient conditions, thereby opening a link to CK signaling.161,162 In addition, under low N conditions, TCP20 induces NPF6.3 expression.163 LBD37 and 38 overexpressing plants show defects in shoot branching, an increase in anthocyanin levels. They have additionally been shown as negative regulators of NIA1 and 2, NFP6.3, NRT2.1, NRT2.2, and 2.5.164 SPL9 transcript levels, meanwhile, alter the levels of NRT2.1 and 2.2, NPF6.3, NIA1 and 2, and NiR.94 In addition to TF-mediated signaling, the role of hormones has also been extensively studied. On one hand, N supply impacts hormone biosynthesis, transport, and signaling, while on the other, hormones have been shown to control N-related transcriptional networks. For example, the influence on auxin,94,165 CK,166,167 ethylene,168 and abscisic acid (ABA)169 of reduced forms of N has been demonstrated. In contrast, hormonal regulation was shown for NPF6.3. For example, auxin- and ethylene-treated seedlings, which displayed an increased level of NPF6.3.170 Another relevant example to be mentioned here is posttranscriptional control of nitrate reductase (NR) by auxin and CKs.171 CKs and NO −3 itself are signals in systemic signaling processes. It has been shown that the shoot generates a CK-mediated systemic signal that reports the NO −3 demand of the whole plant.161 In addition, TCP20 might be involved in systemic N signaling and direct nitrate foraging by Arabidopsis roots.163 Here, insertion mutants of TCP20 lost the capability for root foraging on heterogeneous NO −3 media in split root experiments. Transcriptomic analysis to root foraging in Arabidopsis revealed an additional aspect of systemic NO −3 signaling.161 Here, initial gene expression responded to local N signals, while later responses led to systemic N signals. Shoots were necessary for systemic N signaling. C-terminally encoded PEPTIDES

 Local and systemic signaling of macronutrient limitations

55

(CEPs) were found to play a role in systemic signaling, and were produced in the part of the root that was exposed to low N and translocated to the shoots, where they were bound to CEP receptors.172 A yet unknown shoot-to-root feedback signal then upregulates NRT2.1 expression in the part of the root that has been exposed to high N levels.

Potassium

Signaling in response to changes in K+ concentrations is thought to be initiated by the rapid hyperpolarization of the plasma membrane. It was observed that, independent from the intracellular K+ content, the high-affinity transporter AtHAK5 was induced immediately after the polarization event.173 No K+ sensor has yet been described. It is known, however, that downstream signaling of K+ involves ROS and Ca-mediated signaling.103,174 The connection between hyperpolarization and Ca2+ signaling was further demonstrated in studies showing the induction of those Ca2+ channels that regulate the cytoplasmic Ca2+ concentration.175 Another link to Ca-mediated signaling was established by Ragel et al.,176 where the HAK5 transporter was activated via phosphorylation by a protein complex, consisting of CIPK23 and CBL1, 8, 9, or 10. The phosphorylated form of the transporter showed an increase in uptake speed, as well as a higher affinity towards K+ ions. It is thought that the influx of Ca2+ under K+ limiting conditions leads to the activation of the CBL Ca2+ sensors mediating the phosphorylation of HAK5 by CIPK23.101 CBL 1 and 9 were also shown to be positive regulators of AKT1,102,103 while CBL10 functions as a negative regulator.177 Recently, the direct role of Ca2+ signaling for AKT1 activation as mediated by the CBL1–CIPK23 complex was proven by patch clamp and two-electrode voltage clamp (TEVC) analysis.178 Downstream of the mentioned proteins, the following TFs were identified as playing a role in K+ signaling: DDF2, JLO, bHLH121, TFII A, were all involved in the activation of K+ transporters, such as HAK5.179,180 In the early response to K+-deprivation, K+ transporters are upregulated, shifting low-affinity to high-affinity uptake.181 Hormones, such as jasmonic acid,182,183 ethylene,174 and CKs184 were shown to regulate K+ signaling.

Calcium

Transcriptional responses to Ca2+ deficiency were addressed by Shankar et al.185 The authors report the short- and long-term response of rice to Ca2+-deficiency on the transcriptional level. The differentially regulated genes identified in this study are involved in central metabolic pathways, such as carbohydrate, phosphate, N, and lipid metabolism, and photosynthesis. In addition, members of the Ca-mediated signaling pool, such as Cams, CBLs, CIPKs, and CDPKs, and the Ca2+ transporters CHX, and ACA were also found to be differentially regulated.

Magnesium

Plants respond to Mg2+-deprivation within 1 h through the expression of high-affinity uptake systems.186 This quick response indicates a local, rather than a systemic, sensing event.187 Interestingly, most MRS2 genes in leaves are not differentially upregulated under any experimental Mg2+ condition in Arabidopsis and rice,66,188 which is supported by the observation that Mg2+ redistribution and retranslocation were not observed under Mg2+ deprivation.187 In B. napus, remobilization of Mg2+ occurs from older to younger leaves.189 As mentioned earlier, Mg2+ deprivation leads to an impaired phloem loading of sucrose, resulting in starch accumulation and inhibition of photosynthesis and ROS production, finally leading to reduced growth and development. Genes differentially expressed under Mg2+-deprivation was found to be ABA-responsive. However, no change in ABA levels could be detected.188 ABA was

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CHAPTER 3  Macronutrient sensing and signaling in plants

shown to play a role in Mg2+ toxicity, where ABA levels increased significantly in roots and shoots in response to high Mg2+ levels.190 Ethylene and nitric oxide production were shown to increase under Mg2+ deficiency, accompanied by the stimulation of root hair growth.191 Ethylene was earlier described to be involved in Mg2+ deficiency signaling, as several genes involved in ethylene biosynthesis were shown to be upregulated.188 Recently, NO− was proposed as a control for ethylene synthesis and vice versa, both regulating root hair growth under Mg2+ deprivation.191

Sulfur

SO 24 − is taken up by the root. Transporters identified to be involved in this uptake are located in the epidermal and cortical plasma membrane of the root, and are called SULTRs. The expression of the ­high-affinity uptake transporter Sultr1.1 is regulated by the promotor element SURE (sulfur-responsive element).In addition, it is regulated by a protein phosphatase.192 The SURE promoter element was found in a number of genes required for adaptation to SO 24 − deprivation. Independent of the SURE element, the regulator sulfur limitation 1 (SLIM1) was found to control Sultr1.1, Sultr1.2, and Sultr4.1.193 SLIM1 was also reported to induce miR395 expression.194 MicroRNA395 targets the ATP-sulfurlyase APS genes ATPS1, 3 and 4, and the low-affinity xylem loading transporter Sultr2.1.194,195 MicroRNA395 itself has been shown to be involved in long-distance signaling through the phloem.139 This movement of miR395 resulted in the downregulation of one of its targets in rootstocks, indicating that miR395, in addition to miR399, could act as a long-distance signal under nutrient starvation.133

CONCLUSION AND FUTURE PERSPECTIVES Macronutrients, including P, N, K, Ca, Mg, and S, are essential for plants to complete their l­ ifecycles.1 The individual nutrients have attracted different attention in research as their abundance, and therefore economic importance vary. Nevertheless, numerous studies addressed all macronutrients and plants ­responses under deficient and sufficient conditions. The effects of deficiency and the subsequent responses of the plant depend on plant species, differ between nutrients, and are related to their ­functions in metabolism and also their chemical nature. In general, plants respond early to deficiency by increasing their root surface, while at the same time reducing shoot growth. The limiting conditions impair both growth and yield. Plants evolved complex molecular mechanisms to adapt to deficient ­conditions. The degree of understanding of these pathways involved in sensing and relaying macronutrient deficiency is related to each macronutrient’s importance in agriculture. For example, N and P are often scarce in intensive farming, and therefore many efforts have been made to understand the complex signaling and sensing networks relating to their deficiency. Sensing of the nutrients is discussed as either mediated by external abundance or by internal levels of the corresponding nutrient. However, little is known about the actual functioning of the receptors for each of the respective macronutrients. Nutrients are taken up by the roots and are channeled via the xylem to the shoot. Shoot-to-root translocation and signaling is then mediated by the phloem. Local and systemic downstream signaling cascades are complex and often involve the macronutrients themselves in the role of signaling molecule, enzymes, hormones, miRNAs, and transcription factors. Future work will need to be carried out in order to focus on the sensing of macronutrients and downstream local and systemic signaling in early and late plant responses. Finally, research is required to prove that the understanding of plant responses can eventually be translated into genetically engineered crops that perform better under limiting conditions.

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176. Ragel P, Ródenas R, García-Martín E, et al. CIPK23 regulates HAK5-mediated high-affinity K+ uptake in Arabidopsis roots. Plant Physiol 2015; pp. 01401.2015. 177. Ren X-L, Qi G-N, Feng H-Q, et al. Calcineurin B-like protein CBL10 directly interacts with AKT1 and modulates K+ homeostasis in Arabidopsis. Plant J 2013;74(2):258–66. 178. Behera S, Long Y, Schmitz-Thom I, Wang X-P, Zhang C, Li H, et al. Two spatially and temporally distinct Ca2+ signals convey Arabidopsis thaliana responses to K+ deficiency. New Phytol 2017;213:739–50. 179. Kim MJ, Ruzicka D, Shin R, Schachtman DP. The Arabidopsis AP2/ERF transcription factor RAP2.11 modulates plant response to low-potassium conditions. Mol Plant 2012;5(5):1042–57. 180. Hong JP, Takeshi Y, Kondou Y, Schachtman DP, Matsui M, Shin R. Identification and characterization of transcription factors regulating Arabidopsis HAK5. Plant Cell Physiol 2013;54(9):1478–90. 181. Ashley MK. Plant responses to potassium deficiencies: a role for potassium transport proteins. J Exp Bot 2005;57(2):425–36. 182. Takehisa H, Sato Y, Antonio BA, Nagamura Y. Global transcriptome profile of rice root in response to essential macronutrient deficiency. Plant Signal Behav 2014;8(6):e24409. 183. Schachtman DP. The role of ethylene in plant responses to K+ deficiency. Front Plant Sci 2015;6:1153. 184. Nam Y-J, Tran L-SP, Kojima M, Sakakibara H, Nishiyama R, Shin R. Regulatory roles of cytokinins and cytokinin signaling in response to potassium deficiency in Arabidopsis. PLoS One 2012;7(10):e47797. 185. Shankar A, Srivastava AK, Yadav AK, et al. Whole genome transcriptome analysis of rice seedling reveals alterations in Ca2+ ion signaling and homeostasis in response to Ca2+ deficiency. Cell Calcium 2014;55(3):155–65. 186. Tanoi K, Kobayashi NI, Saito T, et al. Effects of magnesium deficiency on magnesium uptake activity of rice root, evaluated using 28 Mg as a tracer. Plant Soil 2014;384(1–2):69–77. 187. Kobayashi N, Tanoi K. Critical issues in the study of magnesium transport systems and magnesium deficiency symptoms in plants. Int J Mol Sci 2015;16(9):23076–93. 188. Hermans C, Vuylsteke M, Coppens F, Craciun A, Inzé D, Verbruggen N. Early transcriptomic changes induced by magnesium deficiency in Arabidopsis thaliana reveal the alteration of circadian clock gene expression in roots and the triggering of abscisic acid-responsive genes. New Phytol 2010;187(1):119–31. 189. Billard V, Maillard A, Coquet L, et al. Mg deficiency affects leaf Mg remobilization and the proteome in Brassica napus. Plant Physiol Biochem 2016;107:337–43. 190. Guo W, Cong Y, Hussain N, et al. The remodeling of seedling development in response to long-term magnesium toxicity and regulation by ABA–DELLA signaling in Arabidopsis. Plant Cell Physiol 2014;55:1713–26. 191. Liu M, Liu XX, He XL, et al. Ethylene and nitric oxide interact to regulate the magnesium deficiency-induced root hair development in Arabidopsis. New Phytol 2016;213:1242–56. 192. Maruyama-Nakashita A, Nakamura Y, Watanabe-Takahashi A, Inoue E, Yamaya T, Takahashi H. Identification of a novel cis-acting element conferring sulfur deficiency response in Arabidopsis roots. Plant J 2005;42(3):305–14. 193. Maruyama-Nakashita A, Nakamura Y, Tohge T, Saito K, Takahashi H. Arabidopsis SLIM1 Is a central transcriptional regulator of plant sulfur response and metabolism. Plant Cell 2006;18:3235–51. 194. Kawashima CG, Yoshimoto N, Maruyama-Nakashita A, et al. Sulphur starvation induces the expression of microRNA-395 and one of its target genes but in different cell types. Plant J 2009;57:313–21. 195. Liang G, Yang F, Yu D. MicroRNA395 mediates regulation of sulfate accumulation and allocation in Arabidopsis thaliana. Plant J 2010;62:1046–57.

CHAPTER

THE SIGNIFICANCE OF NUTRIENT INTERACTIONS FOR CROP YIELD AND NUTRIENT USE EFFICIENCY

4 Martin Reich

Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands; Secretariat of the German Bioeconomy Council, Berlin, Germany

INTRODUCTION An increase of resource use efficiency in agriculture is an essential part of a transition toward a more sustainable bioeconomy. The increasing demand for food of a growing human population, and the future use of biomass for other bio-based purposes asks for an increased production. Several agricultural revolutions throughout the human history increased the productivity of agriculture, especially the most recent one, which started more than a century ago and is often named the “Green Revolution.” Its success was mainly based on a vast increase of nutrient input, leading to a parallel increase of yield output. The negative consequences include a tremendous environmental footprint and low resource use efficiency. Large proportions of the applied fertilizers are not taken up by the crops and enter natural ecosystems, in which eutrophication has harmful consequences, such as ecosystem degradation and a loss of biodiversity.1,2 These negative effects on ecosystems return negative feed-back on agroecosystems, as their productivity relies on natural ecosystem-services.3 Phosphorus (P) fertilizers contain traces of heavy metals which accumulate in soils and may have negative effects on plants and implications for human nutrition.4–7 At the same time, geological P sources are depleting and prizes are expected to increase.8 Increasing the resource use efficiency and sustainability of agriculture is therefore an urgent challenge for policy, economy, and science. A great proportion of the world’s population of soon 8 billion is the direct result of the increase of agricultural productivity during the first Green Revolution. A second Green Revolution, as part of a transition to a sustainable bioeconomy, has to increase the efficiency of agricultural production to ensure current and future food security and to reduce its environmental footprint. Increasing the nutrient use efficiency (NUE) of agricultural systems can be achieved by advances in nutrient management and plant breeding. Both activities have become highly science-driven but there is still a need for more interdisciplinary approaches, because synchronizing the nutrient supply, with the nutrient demand of plants is one of the core challenges for increasing NUE. Plant–environment interactions are complicating this synchrony and increasing our understanding of their physiological, biochemical, and molecular basis is a crucial step. Additionally, nutrients interact with each other in the soil-plant interface. These interactions are often neglected, when nutrients are studied separately. But understanding how nutrient interactions can influence NUE—positively or negatively—is Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00004-1 Copyright © 2017 Elsevier Inc. All rights reserved.

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an important measure to optimize fertilization and avoid trade-offs for the improvement of NUE for different nutrients. The aim of this chapter is to connect nutrient interactions observed on an agricultural level, which are usually measured in terms of effects on growth and yield, with the physiological basis of nutrient interactions within plants. A review of the most important categories of nutrient interactions in plants will be followed by an evaluation of the most promising crop traits toward an improvement of NUE, accommodating the possible trade-offs arising from nutrient interactions. Integrating the understanding of the physiology and biochemistry of nutrient interactions in plants with agricultural practice has a great potential to achieve a more optimal fertilization and to introduce new traits into crops which improve NUE.

NUTRIENT INTERACTIONS AND CROP PRODUCTION EXCESS FERTILIZATION VERSUS OPTIMAL FERTILIZATION Although scientists, such as Liebscher,9 Mitscherlich,10 Baule,11 and DeVries12 refined the “law of the minimum,” formulated by Sprengel and Von Liebig,13 their concepts are much less in today’s scientists’ minds. The paradigm that one factor at a time is limiting growth and yield is wide spread and still guiding most agricultural practice. This persisting success of the very first version of the “law of the minimum” can of course be explained by its central role during the Green Revolution, in the course of which agricultural productivity experienced an unprecedented increase. Supplying cropping systems with an excess of nitrogen (N), available at low cost through the Haber-Bosch process, boosted crop yields and, as a consequence, human population.14 The “law of diminishing returns” predicted that, with increasing fertilization, the increases in crop yield would decrease proportionally. The fact that parallel advances in technology and other aspects of the production process avoided diminishing returns,15 is widely neglected. The finding, that plant growth underlies limitations was an essential breakthrough, but scientists soon came to the conclusion that plant growth is usually limited by multiple factors at a time.16,17 If it comes to nutrients, colimitation of biomass production in plant communities by several macronutrients at a time has empirically been proven.18–20 Still, the illustration of Liebig’s (Sprengel’s) “law of the minimum” as a leaking barrel, with always one nutrient limiting growth at a time, is very persistent in scientific manuscripts and presentations. Especially Liebscher’s “law of the optimum”9 should have received more attention during the last decades, as an optimal balanced fertilization with macronutrients has a higher potential to increase crop yields and NUE than an excess fertilization, which additionally harms the environment and threatens public health.21 Liebscher’s “law of the optimum” states that a nutrient contributes more to biomass production, the closer the other nutrients are to their optimum and the validity of this assumption was shown to fit with many practical experiments.15 A recent meta-analysis of numerous nutrition studies comes to the conclusion that N and P, the macronutrients which limit growth in most agroecosystems, have strong synergistic effects on plant growth.22 This means, that adding both of them together has stronger effects than adding one separately. Based on this study, Davidson and Howarth23 proposed a novel, more complex visualization of the limitation of biomass production by nutrients, which accommodates the synergistic effect of N and P fertilization. Fig. 4.1 is a redrawn and generalized version of this illustration for all macronutrients. It still is, as are all simplified illustrations, insufficient in displaying the entire complexity. But it illustrates that biomass production is not limited by a sequence of separate limitations. The demand for the different

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FIGURE 4.1  (A) Common way of illustrating Liebig’s “law of the minimum” with always one of the six macronutrients limiting biomass production at a time. (B) A simplified illustration of Liebscher’s “law of the optimum”. Sufficient supply with one macronutrient causes increased demand for another nutrient and only a balanced fertilization can avoid diminishing returns, and cause an efficient increase of biomass production. Excess supply with one macronutrient leads to increased losses and decreased NUE. Partly redrawn from Davidson EA, Howarth RW. Environmental science: nutrients in synergy. Nature 2007; 449:1000–1.23

nutrients underlies feedback effects, meaning that satisfying the demand for one nutrient very quickly leads to an increase in demand for another. This alternation in limitations by different nutrients (and other environmental factors) leads to colimitations and an actual limitation by several nutrients at a time. Balanced supply with essential nutrients increases biomass production synergistically, excess supply with a nutrient leads to losses and a decrease of NUE. Several thorough analyses and reviews exist on the importance of Liebscher’s “law of the optimum” for optimal fertilization, improvement of NUE and sustainable increase of crop yields.24,15,25 They come to the conclusion that the more complex assumptions deriving from a multifactorial dependency of nutrient response curves are closer to reality than a binary model derived from a single-factor limitation following the “law of the minimum.” Kho25 developed a model that replaces the binary concept based on Liebig’s law26 by a more realistic version, based on the “law of the optimum.” Instead of biomass production being only limited by one factor, the model takes the sum of degrees of limitation of several factors into an account.25 These factors, which interactively control crop growth, include all abiotic and biotic factors of the plant’s environment, including the availability of essential nutrients. Fig. 4.2 shows an adapted illustration of the model of Kho,25 with biomass production related to the capture of resources. As stated by Kho,25 the empirically observed effect of the availability of nutrient A on biomass production (solid line) is in fact not a direct causal but a correlative relationship. It results from and depends on the availability of the other essential nutrients, in this example nutrients B-G. The response is linear only as long as the capture of all other potentially limiting nutrients increases proportionally. The observed growth response (solid line) is a consequence of an optimal nutrient supply, not a limitation by one single nutrient. Consequently, the effectivity of nutrient A in promoting growth (i.e., the initial slope of the curve) depends also on the concentration of the other essential nutrients in the soil. And because NUE is the ratio of unit biomass produced per nutrient taken up, the NUE for nutrient A increases, if nutrients B-G are closer to their optimum, that is, less has to be applied to gain the same increase in yield.

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FIGURE 4.2  The Empirically Observed Relationship Between Biomass Production and the Capture of Nutrient A (Solid Line) is in Fact a Correlative Relationship, Resulting From the Proportionally Increasing Capture of all Other Essential Nutrients Adapted from Kho RM. On crop production and the balance of available resources. Agr Ecosyst Environ 2000; 80:71–85.25

Many studies have shown that considering interactions and colimitations between macronutrients is crucial to achieve an optimal fertilization and increased yield production.17 But still, this concept seems not to have reached a general awareness. Most scientific studies on plant nutrition and improvement of NUE still deal with a single nutrient, however, a trend toward interactive studies can be observed. Many agricultural systems are still found to fall behind their potential yield and NUE by following a practice of excess fertilization, mostly with N, and often neglecting an optimal supply with other nutrients and optimal nutrient ratios.28–33 Therefore, more research has to focus on the understanding and a quantification of the impact of nutrient interactions on crop production. Multidisciplinary approaches have to combine model-based agricultural research with plant physiology to connect the interactions between nutrients found in the field with the physiological interactions between nutrients found on a plant level.

UNDERSTANDING NUTRIENT INTERACTIONS IN PLANTS TO IMPROVE NUE AND DECREASE ENVIRONMENTAL FOOTPRINTS It should not be surprising that the relatively simple “law of the minimum” does not sufficiently meet and explain the complex relationship between plant growth and nutrition. Now, at the dawn of a second Green Revolution, one of the main challenges will be to increase the efficiency of agricultural production without decreasing its output, to meet both, the demand of an ever-growing world population and the increasing environmental burden of agricultural practice. The simple view of crop yield being limited by only one factor at a time has to be left behind and Liebscher’s “law of the optimum” should finally gain its deserved attention. As discussed earlier, nutrients may have positive synergistic effects on crop

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FIGURE 4.3  Metabolic and Environmental Control of Crop Nutrition Environmental factors (red) and metabolic demand (green) regulate nutrient uptake, which in turn determines tissue nutrient content and growth. Feedbacks between many processes make the relation between nutrition and growth more complex than generally assumed.

growth, if supplied optimally. Balanced fertilization therefore has the potential to increase NUE. The understanding of the physiological, biochemical, and genetic basis of these nutrient interactions on a plant level can essentially contribute to an optimal fertilization. Additionally, improvements of NUE through crop breeding can be matched to advances in fertilization practices. Research on the different levels of organization agroecosystem, field, and plant34 should be integrated in multidisciplinary approaches to achieve a maximum of progress. An important question will be, if trade-offs exist between the NUE for different nutrients. On a plant level, uptake, accumulation, and translocation of one nutrient depends partly on the availability of the other macronutrients to the plant (Fig. 4.3). It is logical to assume that nutrient interactions also influence NUE and that NUE of different nutrients are interconnected in synergistic or antagonistic ways. Testing the contribution of new traits to NUE should therefore involve an alteration of the concentrations of other nutrients in the soil or media. The study of nutrient interactions in plants provides the knowledge base for an improved NUE via the introduction of new crop traits and optimized fertilization. The subsequent paragraphs will summarize the main categories of nutrient interactions in plants, give examples and pinpoint some of the advances from recent research.

NUTRIENT INTERACTIONS IN PLANTS SYNERGISMS AND ANTAGONISMS BETWEEN NUTRIENTS CAUSED BY IONIC CHARGE Mineral nutrition of plants relies on the energy-driven transport of ions across membranes. The physicochemical laws governing this transport lead to an interaction between the uptake of nutrients, depending on their charge. The uptake and accumulation of one ion has an immediate impact on the uptake of

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other ions through the overall cation–anion balance of the cell.35 Plants have mechanisms to increase or decrease the uptake of a nutrient against the balance of charge if necessary, typically if the respective nutrient is scarce or present in excess. But over a wide range of external nutrient concentrations the uptake by the plant will depend mostly on the outside concentrations and changes in the concentration of one nutrient will change the concentration of other nutrients within the plant. Under optimal fertilization, crops maintain an internal cation–anion balance. An interruption of potassium (K) supply, for example, leads to a decrease of K in the plants and an increase of calcium (Ca), magnesium (Mg), and sodium (Na).36,37 Interestingly, an increased Mg supply does usually not depress K uptake, probably due to the very efficient uptake of K.38,35 Due to its relatively high permeability at the root plasma membrane, K also plays an important role as a counter-cation for nutrients taken up as anions. This might explain why a growth stimulation by nitrate depends on the available amount of K.27 K is also assumed to be the counter-cation for sulfate in vacuoles because sulfate deficiency leads to parallel decreases in sulfur (S) and K contents in cabbage.39 Another well-studied example of synergistic or antagonistic interactions between nutrients due to balance of charge is the effect of the available N source on the plant’s ionome. Two phytoavailable mineral forms of N are present in soils, negatively charged nitrate (NO3−) and positively charged ammonium (NH +4). For the reason that N is the nutrient taken up by plants in the largest amounts, the presence of one or the other form in the soil has unavoidable consequences for the balance of charge. Although plants counteract an excess cation uptake with the excretion of protons (H+) and an excess anion uptake, with the excretion of hydroxyl ions (OH−)40 there are also consequences for the uptake of other nutrients. If nitrate is the dominant form of N taken up, the uptake of other negatively charged ions, for example phosphate or sulfate, is usually decreased. The other way around, supply of plants, with ammonium typically decreases the uptake of other cations, for example K.41,42 The uptake of K is inhibited by ammonium43 but also the translocation and partitioning of K are changed by the form of N present.44 Also the positive interaction between nitrate and K is not restricted to the uptake at the root plasma membrane. The translocation within the plant as well as the storage in the vacuoles of these two nutrients appears to be tightly coupled (see Coskun et al.45 for a recent review). A shift between the forms of available N in soils, for example by changes in soil pH or aeration, has therefore consequences for the mineral nutrition and quality of crops. The other way around, K was shown to improve ammonium assimilation and limit excessive ammonium fluxes at the plasma membrane of paddy rice.46 For cations, many studies have shown that an increase in the supply with either K, Mg, or Ca decreases the uptake and vacuolar storage of the other cations.35,47 Another example is the effect of an excess of Na, that is, salt stress, on the uptake of other nutrients. Na is usually not considered an essential nutrient and taken up by most plants in only minor proportions. But high levels of Na in the soil go along with consequences for the cation balance. Positively charged Na replaces K and Ca from membranes and cell walls, and leads to an efflux of these cations from the cytosol48–50 and lower tissue contents of these nutrients.51 Soil salinity can therefore be expected to have not only an effect on biomass production if salt levels are high, but also on crop quality due to changes in nutrient uptake, even if salt concentrations are below critical levels for growth. Another interaction between nutrients in the form of ions is possible precipitation of anions with cations.52 If precipitation occurs depends on the solubility products and binding constants, and is usually relevant under an excess supply with nutrients or in certain types of soil. Precipitation outside and inside of plants can lead to an immobilization and deficiency of nutrients. An improvement of NUE by increasing the storage of mineral nutrients in cell compartments is therefore also limited by precipitation.

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Uptake and allocation of different nutrients within plants are interrelated, due to interactions caused by ionic charge. If one of these mechanisms is modified in the course of an improvement of NUE for one nutrient by breeding, effects on other nutrients are likely. Increasing the storage capacity of the vacuole for nitrate, which could be a reasonable measure to increase the NUE for N, could decrease the capacity for other anions, such as sulfate and phosphate, and consequently decrease NUE for S and P.

REGULATORY INTERACTIONS In addition to the physicochemical interactions summarized in the previous paragraph there are more complex interactions between nutrients in plants, most of which are not fully understood and many not even discovered, yet. But there is a recent awareness that many mineral nutrients are more than just substrates for transport and assimilation, and fulfill complex regulatory roles in plant metabolism. The function of Ca as an important signaling molecule is known for quite a long time already, and also nitrate appears to be not only the substrate but also a signal for nitrate transport.53–57 There are also examples of nutrients regulating the uptake of other nutrients but themselves. Ca, for example, plays an important role in the regulation of membrane transport of other nutrients, for example K.58 Nitric oxide (NO) and hydrogen sulfide (H2S) are gaseous signal compounds thought to be involved in the regulation of morphological and biochemical processes, and acclimations in plants, including nutrient uptake.59,60 Both compounds also appear to interact with each other, with reactive oxygen species and with other chemically active compounds in a complex signaling network resolved.61 NO and H2S signaling are also suggested to be interconnected with Ca signaling.62,63 And because the production of NO and H2S is dependent on the supply of the plant with N and S, respectively, these gaseous signal compounds are a direct link for regulatory nutrient interactions. This can happen on the level of membrane transport, but also through morphological changes. For example, NO induces lateral root formation64 and is therefore likely to affect not only nitrate uptake, because the uptake of different nutrients depends on root system architecture. In addition direct interaction on a molecular, cellular, or morphological level, demand-driven regulatory interactions may occur between nutrients. Changes in metabolic demand for a nutrient can be caused by environmental cues or the developmental stage of the plant. The synthesis of the necessary metabolic products usually requires a balanced uptake of two or more nutrients. Plants also need to maintain homeostasis of the other nutrients, if one macronutrient limits growth, which requires regulatory interactions. Extensive metabolic and transcriptomic studies reveal the complex transcriptional regulation which mediates responses to nutrient stresses.65 The entire complexity of regulatory nutrient interactions and their molecular basis, however, are still far from being understood. Different groups of proteins, such as kinases and the 14-3-3 protein family,66,67 appear to be involved in the coordination on a molecular level. For crop breeding, the complexity of regulatory interactions between nutrients may lead to unexpected results concerning NUE. An imbalance of nutrient composition by increased uptake of one nutrient might lead to a disturbance, or at least an alteration of regulatory processes with consequences for the uptake and metabolism of other nutrients. The other way around, an optimal supply with beneficial signaling nutrients can improve utilization efficiency for other nutrients, increase growth and enable adequate responses to stresses. And the systemic decryption of the genetic and molecular basis of these regulatory interactions might offer new possible approaches to improve NUE.

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METABOLIC INTERACTIONS Synergistic effects of nutrients on crop yield can also be caused by metabolic interactions. A main reason why the “law of the minimum” (and its illustration as a leaking barrel) is too simple, is that it does not accommodate the fact that an increase of biomass production by one nutrient has a feedback effect on nutrient requirement through a change in metabolic demand (Figs. 4.1 and 4.3). Biomass increase by nutrient addition cannot be seen separate from the resulting changes in nutrient demand. Pathways of macronutrient assimilation are interconnected in many ways, with consequences for crop nutrition. A decrease of N under P limitation could, for example, be mediated by changes in cytokinin levels.68 K is involved in carbon metabolism and nutrient assimilation, because it is necessary for the activity of many enzymes, including some of the nitrate assimilation pathway.69 This could explain why a yield increase by N depends on K supply.45 N and S assimilation are coregulated70 and combined application of N and S has synergistic positive effects on crops.71,72 Another example for a metabolic interaction between nutrients can be found in symbiotic N-fixation. Efficient N-fixation in legumes strongly relies on adequate supply with Ca and P.73,74 Improvement of NUE should take such metabolic interactions between nutrients into account. A systematic improvement of overall NUE can only be achieved if the molecular and physiological mechanisms of the most important interactions between macronutrients are further studied. Separating nutrient interactions based on ionic charge from interactions caused by altered metabolic demand is not trivial. A study on S, for example, found that most interactions of sulfate with other nutrients in cabbage are related to changes in growth, rather than changes in sulfate.39 The study used fumigation with H2S to receive plants which grow normally but are low in S. The results showed that changes in most nutrients under S deficiency disappeared with H2S fumigation. With H2S fumigation the demand of the plant for organic S can be covered75 and most changes in other nutrients under S deficiency were actually caused by a change in growth. Only K remained low, showing the direct interaction with sulfate, probably because K is the counter-cation for sulfate in vacuoles.

INTERACTIVE EFFECTS ON ROOT MORPHOLOGY Plants react to fluctuations in soil nutrient concentrations mainly by increasing the affinity and capacity of their membrane nutrient transport and by alterations in root architecture. The phenotype of root systems was, for a long time, an understudied part of the plant until it recently gained a lot more attention and is studied with newly developed imaging and modeling tools.76–78 Deficiencies of macronutrients have been shown to have divers and nutrient specific effects on root architecture.79–81 N deficiency, for example, leads to deep root systems with spare branches but long secondary roots. P deficiency, in contrast, leads to shallow, highly branched root systems with local, patchy proliferation in regions of the soil, where phosphate is available (see Gruen et al.82 for a thorough review). The different strategies represent adaptations to the physicochemical properties of these nutrients in the soil: Nitrate is relatively mobile, phosphate is very immobile. These effects were, however, mostly studied separately up to now, with one nutrient deficiency at a time. Studies on interactive effects are still rare. An elegant example is a recent study which used binary combinations of macronutrient deficiencies.83 It revealed interactive effects of nutrient deficiencies on root architecture traits and found common gene-clusters correlated to the morphological changes. It still needs to be determined though, if breeding toward an efficient root system for the uptake of one nutrient will reduce the efficiency of taking up other nutrients from the soil. For example, selecting for an efficient root architecture response to fluctuations of nitrate in the soil could result in cultivars with a decreased efficiency of phosphate capture (Fig. 4.4).

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FIGURE 4.4  Improvement of NUE in Crops can Target Different Physiological Processes Nutrients interact in all these processes with each other, consequently synergistic or antagonistic effects can occur.

PROMISING CROP TRAITS TO IMPROVE OVERALL NUE NUTRIENT UPTAKE EFFICIENCY VERSUS NUTRIENT UTILIZATION EFFICIENCY The identification of novel crop traits for an improvement of NUE should be accompanied by a validation of these traits under varying environmental conditions, including varying supply of other macronutrients. Principally, NUE can be improved by improvement of one of the two components it consists of: nutrient uptake efficiency and nutrient utilization efficiency. Due to antagonistic interactions between some macronutrients due to differences in charge it is an important question how successful an improvement of NUE by increasing the uptake efficiency can be. An increased uptake of one nutrient at the plasma membrane or at the tonoplast will change the uptake and accumulation of other nutrients and the result might only be a shift in NUE of different nutrients, but no increase of overall NUE. The same might be true, if uptake capacity of one macronutrient is increased by a change in root architecture. The contrasting requirements of, for example, efficient nitrate and phosphate uptake could present difficult trade-offs. Improvements of nutrient utilization efficiency (ratio of yield to actual nutrient uptake84), in contrast, are promising to increase the NUE for one nutrient without affecting the NUE of others or even to increase the overall NUE. A few of the most promising traits to improve nutrient uptake or utilization efficiency will be reviewed in the following paragraphs, with an emphasis on nutrient interactions.

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INCREASED STORAGE CAPACITY AND REMOBILIZATION EFFICIENCY Nutrient storage and accumulation in plants present an evolutionary strategy to buffer fluctuations in external nutrient concentrations, that is, fertilizer supply. In periods of nutrient excess, cellular storage in vacuoles as well as storage in dedicated organs (e.g., tap roots) serve as sinks for nutrients. Vacuoles are very diverse in their dedicated storage function, depending on plant organ and cell type.85 In vegetative tissue, nutrients can be mobilized from vacuoles and storage organs when external nutrient supply becomes limiting to growth. An increased remobilization of previously stored resources may also be due to the developmental stage of the plant or to unfavorable environmental conditions.86 For the tonoplastic membrane of the vacuole accounts the same as for the root plasma membrane: Fluxes of ions are interrelated, which gives rise to nutrient interactions. Increasing the capacity of the vacuole for one nutrient in order to increase NUE may at the same time diminish its capacity for another nutrient of the same charge. This conflict of charge is avoided if nutrients are stored in an organic, electrically neutral form, such as proteins. A more sensible approach to increase the storage capacity of nutrients is the increased synthesis of nutrient-rich organic metabolites, for example high-S containing proteins for the increased storage of S.87 Also, ribulose 1,5 bisphosphate carboxylase/oxygenase (RubisCO) is thought to function partly as a storage for N which can be remobilized if the demand for N exceeds uptake.88 RubisCO seems to be a storage for S, as well, as indicated by the early remobilization of S from the RubisCO-pool during S deprivation.89 Storage in the form of proteins has additional advantages over storage in the form of mineral ions, such as an avoidance of a conflict with turgor regulation.88 And it displays a more promising way to improve NUE for several macronutrients at the same time, for example N and S, which could otherwise be difficult due to the same charge of nitrate and sulfate. The activity of storage compartments and the resulting sink strength of harvested organs determines yield quality. Contents of the S-containing amino acids cysteine and methionine, for example, are mainly limited by the sink strength of the seeds.90 In seeds, mineral nutrients are mainly stored as phytates91 and in several types of vacuolar compartments, and the endoplasmic reticulum.92 Increasing the sink strength of seeds by increasing the activity of vacuolar transporters bears the potential of improving NUE for several nutrients at the same time. The process of nutrient remobilization from storage compartments was shown to be critical to NUE, as well. The importance of an efficient remobilization of nitrate from storage in stems and roots for NUE was shown, for example, in rice.93 The relatively slow remobilization of sulfate from vacuoles was identified as a bottleneck for S use efficiency in crops.87 If there exist trade-offs in the remobilization efficiency for different nutrients in the form of ions, as it is expected for storage capacity, has to be investigated. But it is clear that an increased storage capacity can only improve NUE if the respective remobilization processes are efficient enough.

EFFICIENT RECYCLING AND ALLOCATION TO YIELD ORGAN Berendse and Aerts94 identified the mean residence time (MRT) of a nutrient in the plant as a determining factor of plant NUE (for a review see Reich et al.34). An increase of MRT can be achieved by effective breakdown, redistribution, and recycling of metabolic products within the plant.95,96 More efficient recycling of nutrients within the plant might bear the greatest potential to increase overall NUE. Given that nutrient uptake is controlled by the internal concentration of the nutrient, an increased MRT may lead to a decreased uptake rate, because less nutrient is needed to produce one unit of biomass. However, the overall nutrient balance between the plant and the soil should remain relatively unaffected and

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effects on the uptake of other nutrients should therefore be limited. Understanding the physiological and molecular basis of the coordination between source and sink leaves during senescence and seed/ fruit formation are a key to improve nutrient recycling efficiency.97 A major task for breeding will be to overcome the apparent trade-off between late senescence (“stay green” phenotypes), which potentially increases yields, and a concurrent decrease of N use efficiency.98 The most important crops for the production of food and feed are grains with seeds as the finally harvested and processed plant organ. Efficient and timely reallocation of nutrients from vegetative to generative tissues determines both, quantity and quality of grain yield.99 Improvement of the mechanisms involved in this process theoretically bears a great potential to improve the overall NUE. Increasing the sink strength by, for example, increasing seed number or size, can lead to improvements, as well as increasing the efficiency of senescence and autophagy.96,100,101 But remobilization of different macronutrients to seeds differs and depends on environmental conditions.98,102 Nutrient deficiency has detrimental effects on crop yield and quality101 but the effects vary depending on the nutrient. While nutrient deficiency usually leads to an accelerated switch from the vegetative to the generative growth phase, that is, seed setting, the opposite is true for phosphate deficiency. The delay is probably caused by the reallocation of metabolic resources to roots in response to P deficiency.103 Naturally, the reallocation of a nutrient to the seeds depends on the content of this nutrient in the vegetative tissue but also on the availability of this nutrient in the soil during seed setting and grain filling.104 And also, protein content and composition of seeds depends on adequate nutrition during and after anthesis.105 The efficiency and abundance of enzymes involved in nutrient remobilization during senescence and allocation to seeds could also be manipulated to increase NUE.106 But the interdependency between different nutrients during recycling and allocation to seeds, and their interactive effects on yield quality parameters have hardly been studied. It needs to be investigated if increasing the reallocation efficiency of one macronutrient would decrease the one of another macronutrient.

ROOT SYSTEM ARCHITECTURE Especially for problematic soils with very low fertility, as found in many developing countries, it is important to breed for specialized root system types which match the most urgent constraints of the different locations (most of all P deficiency, N deficiency, and aluminum toxicity).107 As discussed earlier, root architecture responds differently to deficiencies of the different macronutrients. Especially efficient nitrate and phosphate uptake require very different root systems. A question that arises is, if there are root traits which have the potential to improve NUE for all macronutrients at the same time, or if the very contrasting mobility in the soil of, for example nitrate and phosphate, will always lead to unavoidable trade-offs. A large root system which exploits the soil and forages for immobile and mobile nutrients at the same time would theoretically be superior. It would also buffer local and temporal fluctuations in soil nutrient concentration. High root-to-shoot ratios are usually triggered by nutrient deficiencies but could also be achieved by breeding. A large root system can, however, only improve NUE if it is not on the expense of above ground biomass and yield production. Efficient and timely allocation of resources from the root system to the yield organs would be of great importance. Another theoretical possibility would be a diversified root system which unifies several kinds of root traits, for example the different optima of lateral root branching density for nitrate and phosphate uptake efficiency.108 Parts of the root system could explore a larger volume of the soil to forage for mobile

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nutrients, such as nitrate, while shallow, highly-branched parts would forage for immobile nutrients, such as phosphate. Additionally, the possibility to introduce specialized root traits from wild species, such as root clusters,109 into crops should be investigated. The role of root architecture for efficient nutrient capture from the soil has almost exclusively been studied in response to insufficient nutrient supply. Findings from such studies are highly valid to improve NUE in the nutrient poor agriculture systems of many developing countries. Improving NUE in the well-fertilized, intensive agriculture of industrialized countries, however, probably asks for different breeding strategies. Until now, the potential of root traits to improve NUE has hardly been investigated under well-fertilized conditions. Such studies would be very important for breeding of crops with improved NUE and a consequent decrease of fertilizer input in intensive agriculture.

EFFECTIVE UTILIZATION OF INCREASED ATMOSPHERIC CO2 Increasing atmospheric levels of CO2 (eCO2) have the potential to stimulate photosynthesis and crop yield in the future, although the extend of this stimulation will depend on other environmental factors, such as water availability and temperature.42,110–113 Crop breeding should take a higher carbon availability in a future atmosphere into account, because it bears the potential of improving NUE by increasing the efficiency of carbon fixation.114,115 Also, an improved nutrient capture could be achieved if additional carbon was, for example, rerouted to the roots. A larger root system, as described earlier, could be supported with additional photosynthates in an atmosphere with eCO2. In addition an increased biomass, a decline in mineral nutrient content of plants exposed to eCO2 is observed.116 And other key parameters of yield quality were reported to be negatively affected under eCO2, as well.117 These effects depend, like the stimulation of biomass, on other environmental factors, such as water availability, temperature, and N supply.42,118 Crop breeding should take measures to prevent these negative effects while increasing the potential of crops to utilize the additional carbon to increase their nutrient uptake and utilization efficiency and thereby their overall NUE. It should also be investigated if there exists an unavoidable trade-off between an increase in NUE caused by increased carbon assimilation and a decrease in yield quality. Different mechanisms are suggested to be responsible for the decline of mineral nutrients in plants under elevated CO2. A decreased transpiration flow appears to be responsible for decreased uptake of mobile nutrients, such as K,119 and a decreased translocation of Ca and Mg to the shoot via the xylem sap.120 Phosphate uptake can be increased by elevated CO2, due to increased root biomass.119 Nitrate assimilation is supposed to be inhibited by CO2.121 Additionally, altered physiological requirements under elevated CO2 can lead to changes in nutrient allocation in the plant.122 As different mechanisms seem to be responsible for the decline of different nutrients under elevated CO2, nutrient interactions might be of increased importance when it comes to an improvement of NUE in a future atmosphere.

CONCLUSIONS Advances in soil management practices and crop breeding have to go hand in hand to improve the NUE of agricultural systems. Synchronizing nutrient input with the nutrient demand of crops is one of the most promising approaches to minimize nutrient losses from the field.123 Modern breeding should aim to understand and exploit the genetic variation for NUE within crops124 by, for example, exploring

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natural variation of model species125 and by an introduction of beneficial traits from wild relatives or old landraces into modern crop germplasms.126 Changes in nutrient management combined with successful breeding of crops with higher NUE have the potential to significantly contribute to food security for coming generations.127 Also, the combination and careful balance of inorganic with organic fertilizers promises further increases in crop productivity and NUE in many areas.128 Due to the complexity that arises from the interactive control of crop production by metabolic and environmental factors,34 these transitions in agricultural practice have to be guided by the insights delivered from fundamental scientific research. The complex interdependency within the plant’s ionome129,130 makes an optimization of plant nutrition in modern agriculture a more complex challenge than the maximization of biomass production following the “law of the minimum” has been. Advanced techniques are necessary, including both, advanced theoretical modeling as well as new practical approaches to effectively integrate genomics, with ionomics and crop phenotyping. These technological advances will be most effective if our fundamental physiological understanding of nutrient interactions in plants further increases.

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109. Lambers H, Shane MW, Cramer MD, Pearse SJ, Veneklaas EJ. Root structure and functioning for efficient acquisition of phosphorus: matching morphological and physiological traits. Annals Bot 2006;98:693–713. 110. Wang D, Heckathorn SA, Wang X, Philpott SM. A meta-analysis of plant physiological and growth responses to temperature and elevated CO2. Oecologia 2012;169:1–13. 111. Xu Z, Shimizu H, Yagasaki Y, Ito S, Zheng Y, Zhou G. Interactive effects of elevated CO2, drought, and warming on plants. J Plant Growth Regul 2013;32:692–707. 112. Deryng D, Conway D, Ramankutty N, Price J, Warren R. Global crop yield response to extreme heat stress under multiple climate change futures. Environ Res Letters 2014;9:034011. 113. van der Kooi CJ, Reich M, Löw M, De Kok LJ, Tausz M. Growth and yield stimulation under elevated CO 2 and drought: a meta-analysis on crops. Environ Exp Bot 2016;122:150–7. 114. Drake BG, Gonzàlez-Meler MA, Long SP. More efficient plants: a consequence of rising atmospheric CO2? Ann Rev Plant Biol 1997;48:609–39. 115. Tausz-Posch S, Armstrong R, Tausz M. Nutrient use and nutrient use efficiency of crops in a high CO2 atmosphere. In Nutrient Use Efficiency in Plants, New York, NY, USA: Springer International Publishing; 2014. pp. 229–52. 116. Loladze I. Hidden shift of the ionome of plants exposed to elevated CO2 depletes minerals at the base of human nutrition. Elife 2014;3:e02245. 117. Högy P, Wieser H, Köhler P, Schwadorf K, Breuer J, Erbs M, et al. Does elevated atmospheric CO2 allow for sufficient wheat grain quality in the future? J Appl Bot Food Qual 2012;82:114–21. 118. Erbs M, Manderscheid R, Hüther L, Schenderlein A, Wieser H, Dänicke S. Free-air CO2 enrichment modifies maize quality only under drought stress. Agron Sustain Dev 2015;35:203–12. 119. Vuuren MM, Robinson D, Fitter AH, Chasalow SD, Williamson L, Raven JA. Effects of elevated atmospheric CO2 and soil water availability on root biomass, root length, and N, P and K uptake by wheat. New Phytol 1997;135:455–65. 120. Houshmandfar A, Fitzgerald GJ, Tausz M. Elevated CO2 decreases both transpiration flow and concentrations of Ca and Mg in the xylem sap of wheat. J Plant Phys 2015;174:157–60. 121. Bloom AJ, Burger M, Asensio JSR, Cousins AB. Carbon dioxide enrichment inhibits nitrate assimilation in wheat and Arabidopsis. Science 2010;328:899–903. 122. McGrath JM, Lobell DB. Reduction of transpiration and altered nutrient allocation contribute to nutrient decline of crops grown in elevated CO2 concentrations. Plant Cell Environ 2013;36:697–705. 123. Cassman KG, Dobermann A, Walters DT. Agroecosystems, nitrogen-use efficiency, and nitrogen management. AMBIO 2012;31:132–40. 124. Hirel B, Le Gouis J, Ney B, Gallais A. The challenge of improving nitrogen use efficiency in crop plants: towards a more central role for genetic variability and quantitative genetics within integrated approaches. J Exp Bot 2007;58:2369–87. 125. Chietera G, Chardon F. Natural variation as a tool to investigate nutrient use efficiency in plants. Nutrient Use Efficiency in Plants. New York, NY, USA: Springer International Publishing; 2014. p. 29–50. 126. Tester M, Langridge P. Breeding technologies to increase crop production in a changing world. Science 2010;327:818–22. 127. Mueller ND, Gerber JS, Johnston M, Ray DK, Ramankutty N, Foley JA. Closing yield gaps through nutrient and water management. Nature 2012;490:254–7. 128. Vanlauwe B, Kihara J, Chivenge P, Pypers P, Coe R, Six J. Agronomic use efficiency of N fertilizer in maizebased systems in sub-Saharan Africa within the context of integrated soil fertility management. Plant Soil 2011;339:35–50. 129. Baxter I. Should we treat the ionome as a combination of individual elements, or should we be deriving novel combined traits? J Exp Bot 2015;66:2127–31. 130. Huang XY, Salt DE. Plant ionomics: from elemental profiling to environmental adaptation. Mol Plant 2016;9:787–97.

CHAPTER

THE CONTRIBUTION OF ROOT SYSTEMS TO PLANT NUTRIENT ACQUISITION

5 Erin E. Sparks, Philip N. Benfey

Howard Hughes Medical Institute, Duke University, Durham, NC, United States

INTRODUCTION Mineral nutrition is the basis for plant productivity and health. Many essential nutrients are obtained through soil reserves; however, soils vary in their ability to sustain plant growth. Half of the world’s land is not fit for cultivation and the other half is only partially arable.1 To overcome the limitations of existing soil reserves, exogenous nutrients in the form of fertilizer are added. However, the runoff of these resources can have devastating impacts on the environment and are wasted without plants that can efficiently acquire and utilize the nutrients.2 The amount of a given nutrient that a plant will acquire depends on several factors, including the soil availability, the root system structural features, the plant stores of the nutrient, and the efficiency of nutrient uptake and utilization. Each of these aspects has been studied in depth, but for the purpose of this chapter we focus on the role of the root system structural features, specifically root system architecture (RSA), root morphology, and root anatomy, in nitrogen and phosphorus acquisition. Before discussing the role of these structural features in nutrient utilization it is important to define some root terms. RSA refers to a holistic view of the spatial arrangement of roots within their growth environment.3,4 RSA relies on, but is independent of, smaller-scale features, such as root morphology and anatomy (Fig. 5.1). Root morphology refers to the surface features of the root (e.g., root hairs), whereas anatomy refers to the internal cellular organization and structure. Each of these features has been shown to play a role in plant nutrient acquisition and utilization. Soil macronutrients can be divided into primary (i.e., nitrogen, phosphorus, and potassium) and secondary (i.e., calcium, magnesium, and sulfur). As primary macronutrients are the most critical for plant productivity, this discussion focuses on the role of root architecture in primary macronutrient acquisition.

MACRONUTRIENT LOCALIZATION AND MOBILITY Primary macronutrients vary in their mobility and location within the soil profile. This variation is important to consider when defining the ideal root architecture (ideotype) for nutrient acquisition. In this section we will give a brief overview of the aspects of nitrogen, phosphorus, and potassium availability that influence root architectural considerations. Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00005-3 Copyright © 2017 Elsevier Inc. All rights reserved.

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FIGURE 5.1  Structural Features of Root Systems Root System Architecture (RSA) refers to the spatial organization of roots within their growth substrate, illustrated here with a rice plant. Root morphology refers to the surface features of the root (e.g., root hairs). Root anatomy refers to the internal cellular organization and structure. In this illustration, some monocot tissues are highlighted with different colors (gray, epidermis; green, cortex; red, endodermis; yellow, stele/vasculature; blue, root cap).

Nitrogen is the most abundant nutrient in plant tissues, accounting for 1.5% of the total plant dry matter.5 Nitrogen is highly mobile and acquired from three different sources in the soil: nitrate ( NO3− ), ammonium ( NH +4 ), and amino acids. Although the soil mobility of nitrogen means that it is accessible in all soil profiles, there is a major concern for nitrogen leaching in environmental systems, which concentrates this nutrient into deeper soil profiles. Potassium is the second most abundant nutrient in plant tissues, accounting for 1% of the total plant dry matter, whereas phosphorus accounts for approximately 0.2% of the total plant dry matter.5 Both potassium and phosphorus are less mobile in the soil than nitrogen, but potassium is more mobile than phosphate. The extreme immobility of phosphorus can be attributed to the phosphate ion absorbing strongly to surfaces dominated by Al3+, Fe3+, and Ca2+, which form insoluble and immobile complexes within the soil. Although these complexes are immobile, phosphorus can still be readily obtained when roots come in contact with them. Despite the importance of potassium in plants, there is little known about an advantageous root architecture, morphology, or anatomy for potassium use efficiency. This lack of emphasis is likely because plants are adept at redistributing potassium internally to match demand and thus already highly efficient at potassium assimilation.6 Therefore, we will focus on the role of root systems in nitrogen and phosphorus utility for the majority of this chapter.

METHODS TO ANALYZE THE ROOT SYSTEM ARCHITECTURE

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METHODS TO ANALYZE THE ROOT SYSTEM ARCHITECTURE RESPONSE TO SOIL NUTRIENTS Despite our knowledge of macronutrient availability and heterogeneity within the soil profile, the majority of studies aimed at understanding the nutrient utilization response have profiled roots in a homogeneous monoculture system. This is because obtaining quantitative measurements of the root system under varying nutrient conditions has several challenges. These challenges include the inaccessibility of the root system for imaging and the challenge of generating realistic, but controlled, environments to test the response to nutrient cues. The first problem of root imaging exists for all root system analyses, regardless of nutrient status. The ideal root-phenotyping platform, which would allow continual field-based root imaging, does not exist. Instead a variety of platforms from the laboratory to the greenhouse to the field are used, each with its own limitations.7 In general, lab-based approaches are highly controlled, but lack immediate relevance for agricultural or ecosystem performance, while field-based approaches are highly relevant, but lack the controlled growth conditions required to evaluate the contribution of individual nutrient cues (Fig. 5.2). Much of what we know about the root response to nutrient cues comes from plants grown in homogenous conditions on agar plates with deficiency or sufficiency of a specific nutrient. For larger or more mature root systems, a cigar/paper roll8,9 or hydroponic10 growth system has been used. While these approaches have lead to many advances in our understanding of the molecular pathways contributing

FIGURE 5.2  Trade-Offs With Root-Phenotyping Approaches Due to their subterranean growth, root systems provide a unique phenotyping challenge. Approaches to quantify root traits can be divided into laboratory-based (e.g., growth on agar plates), greenhouse-based (e.g., growth in pots), and field-based. These approaches have trade-offs in terms of growth conditions and relevance. Laboratory-based approaches enable a highly controlled growth environment, but the translational relevance to the field is low. In contrast, field-based analyses are highly relevant for agricultural purposes, but are much more difficult to control.

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to nutrient responses,11 they have limited extrapolation to the heterogeneous soil environment. To simulate a more heterogeneous environment, some studies have supplemented soil with varying levels of nutrients,12,13 with the assumption that the vertical distribution of nutrients is related to the surface addition of fertilizer. By far the most common approach to assay the effect of differential nutrient cues on root systems is the split root assay. In this system, the roots of the same plant are split between two different controlled environments. To split the roots, plants are generally grown on standard media for several days allowing lateral roots to develop. The root system is then cut back until only the two most shootward lateral roots remain. These laterals are allowed to grow out for several days and then transferred to a split system (e.g., plates or pots) in which differential nutrient-containing media are present on either side of the split.14 In a similar, albeit less widely used, approach different nutrient solutions are dripped onto root quadrants to establish a heterogeneous growth system.15 These approaches are advantageous for understanding both the local and global response to heterogeneous nutrient cues.

ROOT SYSTEM ARCHITECTURE IN RESPONSE TO SOIL NUTRIENTS There are two main types of root systems in plants: the tap root system of dicots and the fibrous root system of monocots. Tap root systems are composed of a single seedborne primary root, basal/anchor roots arising from the root–shoot junction, and their associated lateral roots (e.g., Arabidopsis thaliana or legumes) (Fig. 5.3). In contrast, fibrous root systems are composed of a primary root and may also include additional seedborne roots called seminal roots, and below-ground shootborne roots called crown roots (e.g., maize, barley, or rice) (Fig. 5.3). Both types of root systems have a common RSA that is most advantageous for nutrient acquisition in a given soil environment, even if the root types contributing to that

FIGURE 5.3  Root Types in Dicots and Monocots There are two main types of root systems in plants: the tap root system of dicots and the fibrous root system of monocots. Both root systems have an initial, seedborne primary root. The tap root system then forms basal or anchor roots from the root–shoot junction. In contrast, the fibrous root system has additional seedborne roots called seminal roots, and below-ground shootborne roots called crown roots. Each of these root types will produce several orders of lateral roots as well.

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FIGURE 5.4  RSA Ideotypes for Nitrogen and Phosphorus Acquisition RSA ideotypes for nutrient acquisition are illustrated here with a dicot tap root system, such as common bean. A deep RSA is advantageous for nitrogen acquisition under leaching environments and to access deep-water reserves. In contrast, a topsoil foraging RSA is advantageous under low phosphorus conditions because immobile phosphorus is concentrated in the topsoil.

RSA may differ. We will discuss the global and local responses of these two types of root systems in the acquisition of nitrogen and phosphate. The size and distribution of the root system influence the ability to acquire and utilize soil resources. Given the different soil profiles and mobility of nitrogen and phosphorus, there are opposing RSA ideotypes that are considered advantageous. Phosphorus is concentrated in the topsoil, so an RSA that promotes topsoil foraging is the most advantageous (Fig. 5.4). This was originally defined in the tap root system of the common bean16 and extended to include the fibrous root systems of maize17 (Fig. 5.4). In contrast, an advantageous RSA for nitrogen acquisition varies depending on the soil environment. For example, in environments with a high potential for leaching, increased early root density in the topsoil is predicted to reduce the total nitrate leached and improve nutrient acquisition.18 However, when leaching occurs, root depth determines the ability to intercept the nitrogen that is leaching.19 Thus, a “steep, cheap, and deep” ideotype for the maize root system has been proposed.20 The premise of this ideotype is that roots with exploitation of deep soil will optimize both water and nitrogen capture. To achieve a deep root system, breeding efforts focus on producing an RSA that is optimized for water and nitrogen acquisition. This RSA is complemented with advantageous morphology and anatomy of each root type, which we will discuss in the subsequent sections. RSA is impacted in part by local responses to heterogeneous nutrient availability. Locally, there is a proliferation of lateral roots in areas of nutrient sufficiency for both phosphorus and nitrogen.21 This response is easy to reconcile when considering the immobility of phosphorus, where proliferation of roots will facilitate increased accessibility and acquisition of phosphorus. However, it remains poorly understood as to why there is proliferation in areas of high nitrogen, as no advantage has been shown for nitrogen capture.22–24 One argument is that the proliferation enables growth toward the location where other nutrients might also be available.25 An alternative hypothesis that has garnered more support is that root proliferation is beneficial when plants are competing for resources.6 Studies of nutrient acquisition are often focused on a single plant in isolation; however, there is substantial evidence that neighbors influence the root system. For example, when two bean plants were grown with their root

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FIGURE 5.5  Root Systems Compete for Resources at the Expense of Yield The advantage of root proliferation in the presence of sufficient nutrients (specifically nitrogen) is unclear. One hypothesis is that proliferation aids in the acquisition of resources during plant–plant competition. In a study from Maina et al.,26 bean plants grown in individual pots (owner) versus splitting the roots of two plants between two pots (fence sitter) had dramatic effects on root proliferation and yield in the latter. In the presence of a neighboring plant, the beans increased root proliferation and decreased yield compared to the individual potted plants.

systems split evenly between two pots, there was increased root proliferation at the expense of yield compared to plants grown individually within a single pot26 (Fig. 5.5). This suggests that in the presence of neighboring plants, roots proliferate to maximize their nutrient acquisition for a competitive advantage. There are two different facets to consider when identifying the optimal RSA for nutrient acquisition: the fundamental RSA of a plant under unperturbed conditions and the plastic response of the RSA to changing environments. Both have been shown to be advantageous under nutrient-limited conditions.27 However, plasticity is likely the most desirable trait, as it will enable the root to adapt to multiple soil environments. Plasticity can be again subdivided to compare the scale (total size of the root system) and precision (new growth) of the response.15 It remains controversial whether plants benefit more from the scale or precision of the RSA during competition for soil nutrients.6

ROOT SYSTEM MORPHOLOGY AND ANATOMY THAT CONTRIBUTE TO ADVANTAGEOUS NUTRIENT FORAGING In addition to the size and distribution of the RSA, there are morphological and anatomical features of the root system that can be advantageous for optimal nutrient acquisition. The most common morphological feature that is advantageous for nutrient acquisition is the root hair. Root hairs are cylindrical outgrowths forming from a subset of epidermal cells on the surface of the root28 (Fig. 5.1). Root hairs provide several advantages for nutrient acquisition. One advantage is the increase in the absorptive surface area of the root.29 Forming and maintaining new roots (e.g., laterals) is expensive, whereas the length, number, and size of root hairs is an inexpensive way to maximize nutrient acquisition. Indeed,

GENETIC REGULATION OF ROOT SYSTEM ARCHITECTURE

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FIGURE 5.6  Root Cortical Aerenchyma Aerenchyma refers to enlarged gaseous spaces in plant tissues that are formed from cell death or cell separation. Illustrated here is the cortical aerenchyma of a monocot root system. On the left is a cross section of a root with no cortical aerenchyma (cortex indicated in blue). On the right is a cross section of a root with cortical aerenchyma formation (cortex indicated in blue and aerenchyma in red).

Brown et al.30 used mathematical modeling and experimental data to show that root hairs have the greatest potential for phosphorus acquisition relative to the cost of production. In addition, root hairs have been shown to improve the ability of roots to penetrate the soil, which is beneficial for exploration in compacted soils.31 In the “steep, cheap, and deep” ideotype for maize root systems, long root hairs on seminal roots are predicted to be advantageous.20 The major anatomical feature that has been shown to contribute to nutrient acquisition is root cortical aerenchyma formation. Aerenchyma is a tissue consisting of enlarged, gaseous spaces formed by cell death or cell separation32 (Fig. 5.6). It has been shown that root cortical aerenchyma formation is advantageous under nutrient-limited conditions because it maximizes root exploration by lowering metabolic demand.33 However, the advantage of root cortical aerenchyma depends on other root traits. For example, in maize plants grown under low phosphorus, root cortical aerenchyma is more advantageous when lateral root density increases.33 Indeed these observations of the benefit of root cortical aerenchyma were integrated in the “steep, cheap, and deep” ideotype for maize roots.20

GENETIC REGULATION OF ROOT SYSTEM ARCHITECTURE CHANGES IN RESPONSE TO SOIL NUTRIENTS Although root architecture, morphology, and anatomy have all been implicated in optimizing nutrient acquisition, we still know little about the genetic regulation of these structural features. This is mostly attributed to the complex genetic nature of root structural traits. To identify the regions of the genome that contribute to root architecture, quantitative trait loci (QTL) mapping has be used. In QTL mapping, each QTL is statistically determined to account for a specific amount of the variation observed in the given trait. A major QTL, accounting for 27.9% of the variation in phosphorus uptake,34 was mapped in rice for increased phosphorus uptake in phosphorus-deficient soils.35,36 Over 10 years later, the gene underlying this QTL was identified as PHOSPHORUS-STARVATION TOLERANCE 1 (PSTOL1).37 The delay in gene identification was caused by an absence of the PSTOL1 gene from the reference genome.

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PSTOL1 is a serine/threonine protein kinase that enhances early crown root growth, which enables plants to acquire more phosphorus.37 This is an example of a static RSA that enhances nutrient acquisition, as the presence of PSTOL1 constitutively alters the root system.37 An example of the regulation of RSA plasticity in response to local nutrient availability is a recent study that identified a root–shoot–root signal relay in Arabidopsis, which transmits the local nitrogen availability globally.38 Using a split root assay, the authors identified upregulation of C-TERMINALLY ENCODED PEPTIDES (CEP) in response to local nitrogen deprivation. The CEPs are transported to the shoot where they interact with their receptors (CEPRs) and then transmit an unknown signal back to the roots, which increases lateral root growth in areas of sufficient nitrogen.38 Through this peptide– receptor relay the plant can transmit local nutrient availability globally and promote root proliferation in areas of sufficient nutrients. Recall that RSA relies on, but is independent of, morphology and anatomy. We understand more about the molecular control of morphology and anatomy because these features are regulated through fundamental developmental pathways. For example, the root hair responses to nutrient availability are regulated through the direct modulation of endogenous cell fate regulators.39 Similarly, the formation of root cortical aerenchyma is through the modulation of endogenous cell death pathways.32

INTEGRATION OF NUTRIENT SIGNALS A major challenge is to understand the role of the root system in the acquisition of multiple, competing resources. As highlighted earlier, an RSA that promotes topsoil foraging is advantageous under low phosphorus; however, the same RSA would not promote acquisition of deep-water resources or nitrogen in a leaching environment (Fig. 5.4). In bean plants grown under low phosphorus and low water, a dimorphic root system that optimizes both topsoil foraging and deep-water exploration is advantageous.13,40 In A. thaliana, a study of the interaction of multiple resources identified root type–specific architectural and transcriptional responses.41 Resource combinations included nitrogen, phosphorus, potassium, sulfate, and light (as a proxy for carbon). Key findings from this study include a prioritization for low-phosphorus responses and RSA responses to low potassium and sulfate only through interactions with other nutrient deficiencies.41 These results provide the basis for future studies to identify the RSA that are advantageous under competing resource acquisition. It is likely that root system plasticity, which enables the response to diverse and heterogeneous nutrient cues, will be advantageous for improved nutrient use efficiency.

CONCLUSIONS While there are many aspects of the plant and its roots that influence the ability to acquire and utilize soil nutrients, among the most critical are the root system structural features that we have highlighted. Without the ability to locate and respond to nutrient deficit or sufficiency, nutrient uptake and utilization efficiency are less impactful. Further, while our understanding of the root system features that are advantageous for nitrogen and phosphorus acquisition are well accepted, we still understand little about how roots respond to the heterogeneous availability of multiple nutrients or the interplay of nutrient

REFERENCES

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availability with abiotic and biotic stresses. These areas of research will continue to be important as we aim to match root systems to their soil environment.42

ACKNOWLEDGMENTS We would like to thank members of the Benfey lab for providing feedback on this chapter.

REFERENCES 1. Kellogg CE, Orvedal AC. World food possibilities and fertility status of our soils. In: Truog E, editor. Mineral nutrition of plants. Madison: University of Wisconsin Press; 1951. 2. Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl 1998;8:559–68. 3. Lynch J. Root architecture and plant productivity. Plant Physiol 1995;109:7–13. 4. Jung JKH, McCouch S. Getting to the roots of it: genetic and hormonal control of root architecture. Front Plant Sci 2013;4:1–32. 5. Epstein E. Mineral metabolism. In: Bonner J, Varner JE, editors. Plant biochemistry. London: Academic Press; 1965. 6. Hodge A. The plastic plant: root responses to heterogeneous supplies of nutrients. New Phytol 2004;162:9–24. 7. Paez-Garcia A, Motes C, Scheible W-R, Chen R, Blancaflor E, Monteros M. Root traits and phenotyping strategies for plant improvement. Plants 2015;4:334–55. 8. Pan W, Jackson W. Nitrate uptake and partitioning by corn (Zea mays L.) root systems and associated morphological differences among genotypes and stages of root development. J Exp Bot 1985;36:1341–51. 9. Zhu J, Kaeppler SM, Lynch JP. Mapping of QTLs for lateral root branching and length in maize (Zea mays L.) under differential phosphorus supply. Theor Appl Genet 2005;111:688–95. 10. Uga Y, Okuno K, Yano M. Dro1, a major QTL involved in deep rooting of rice under upland field conditions. J Exp Bot 2011;62:2485–94. 11. Canales J, Moyano TC, Villarroel E, Gutierrez RA. Systems analysis of transcriptome data provides new hypotheses about Arabidopsis root response to nitrate treatments. Front Plant Sci 2014;5:1–14. 12. Eghball B, Maranville JW. Root development and nitrogen influx of corn genotypes grown under combined drought and nitrogen stresses. Agron J 1993;85:147–52. 13. Ho MD, Rosas JC, Brown KM, Lynch JP. Root architectural tradeoffs for water and phosphorus acquisition. Functional Plant Biol 2005;32:737–48. 14. Kassaw TK, Frugoli JA. Simple and efficient methods to generate split roots and grafted plants useful for longdistance signaling studies in Medicago truncatula and other small plants. Plant Methods 2012;8:38. 15. Campbell BD, Grime JP, Mackey JML. A trade-off between scale and precision in resource foraging. Oecologia 1991;87:532–8. 16. Lynch JP, Brown KM. Topsoil foraging—an architectural adaptation of plants to low phosphorus availability. Plant Soil 2001;237:225–37. 17. Zhu J, Kaeppler SM, Lynch JP. Topsoil foraging and phosphorus acquisition efficiency in maize (Zea mays). Funct Plant Biol 2005;32:749–62. 18. Dunbabin V, Diggle A, Rengel Z. Is there an optimal root architecture for nitrate capture in leaching environments? Plant Cell Environ 2003;26:835–44. 19. Gastal F, Lemaire G. N uptake and distribution in crops: an agronomical and ecophysiological perspective. J Exp Bot 2002;53:789–99.

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20. Lynch JP. Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann Bot 2013;112:347–57. 21. Drew MC. Comparison of the effects of a localised supply of phosphate, nitrate, ammonium and potassium on the growth of the seminal root system, and the shoot, in barley. New Phytol 1975;75:479–90. 22. van Vuuren MMI, Robinson D, Griffiths BS. Nutrient inflow and root proliferation during the exploitation of a temporally and spatially discrete source of nitrogen in soil. Plant Soil 1996;178:185–92. 23. Fransen B, de Kroon H, Berendse F. Root morphological plasticity and nutrient acquisition of perennial grass species from habitats of different nutrient availability. Oecologia 1998;115:351–8. 24. Hodge A, Stewart J, Robinson D, Griffiths BS, Fitter AH. Root proliferation, soil fauna and plant nitrogen capture from nutrient rich patches in soil. New Phytol 1998;139:479–94. 25. Zhang H, Forde BG. An Arabidopsis MADS box gene that controls nutrient-induced changes in root architecture. Science 1998;279:407–9. 26. Maina GG, Brown JS, Gersani M. Intra-plant versus inter-plant root competition in beans: avoidance, resource matching or tragedy of the commons. Plant Ecol 2002;160:235–47. 27. Trachsel S, Kaeppler SM, Brown KM, Lynch JP. Maize root growth angles become steeper under low N conditions. Field Crop Res 2013;140:18–31. 28. Grierson C, Nielsen E, Ketelaarc T, Schiefelbein J. Root hairs. Arabidopsis Book 2014;12:e0172. 29. Miller AJ, Cramer MD. Root nitrogen acquisition and assimilation. Plant Soil 2004;274:1–36. 30. Brown LK, George TS, Dupuy LX, White PJ. A conceptual model of root hair ideotypes for future agricultural environments: what combination of traits should be targeted to cope with limited P availability? Ann Bot 2013;112:317–30. 31. Haling RE, Brown LK, Bengough AG, Young IM, Hallett PD, White PJ, et al. Root hairs improve root penetration, root-soil contact, and phosphorus acquisition in soils of different strength. J Exp Bot 2013;64:3711–21. 32. Evans DE. Aerenchyma formation. New Phytol 2003;161:35–49. 33. Postma JA, Lynch JP. Root cortical aerenchyma enhances the growth of maize on soils with suboptimal availability of nitrogen, phosphorus, and potassium. Plant Physiol 2011;156:1190–201. 34. Wissuwa M, Yano M, Ae N. Mapping of QTLs for phosphorus-deficiency tolerance in rice (Oryza sativa L.). Theor Appl Genet 1998;97:777–83. 35. Wissuwa M, Ae N. Further characterization of two QTLs that increase phosphorus uptake of rice (Oryza sativa L.) under phosphorus deficiency. Plant Soil 2001;237:275–86. 36. Wissuwa M, Ae N. Genotypic variation for tolerance to phosphorus deficiency in rice and the potential for its exploitation in rice improvement. Plant Breed 2001;120:43–8. 37. Gamuyao R, Chin JH, Pariasca-Tanaka J, Pesaresi P, Catausan S, Dalid C, et al. The protein kinase Pstol1 from traditional rice confers tolerance of phosphorus deficiency. Nature 2012;488:535–9. 38. Tabata R, Sumida K, Yoshii T, Ohyama K, Shinohara H, Matsubayashi Y. Perception of root-derived peptides by shoot LRR-RKs mediates systemic N-demand signaling. Science 2014;346:343–6. 39. Giehl RFH, Wiren von N. Root nutrient foraging. Plant Phys 2014;166:509–17. 40. Ho MD, McCannon BC, Lynch JP. Optimization modeling of plant root architecture for water and phosphorus acquisition. J Theor Biol 2004;226:331–40. 41. Kellermeier F, Armengaud P, Seditas TJ, Danku J, Salt DE, Amtmann A. Analysis of the root system architecture of Arabidopsis provides a quantitative readout of crosstalk between nutritional signals. Plant Cell 2014;26:1480–96. 42. White PJ, George TS, Gregory PJ, Bengough AG, Hallett PD, McKenzie BM. Matching roots to their environment. Ann Bot 2013;112:207–22.

CHAPTER

MOLECULAR GENETICS TO DISCOVER AND IMPROVE NITROGEN USE EFFICIENCY IN CROP PLANTS

6

Darren Plett, Trevor Garnett, Mamoru Okamoto University of Adelaide, Adelaide, SA, Australia

INTRODUCTION As the mineral nutrient required by plants, in the largest amount nitrogen (N), is a major driver of yield and quality in cereals.1 Over 100 million tonnes of N fertilizer is applied in agriculture each year, with N use a major input cost for farmers.2 N fertilizer costs are volatile because of the dependence on costs of the fossil fuel used in their production and this is unlikely to change in the future. Unfortunately, the recovery of the applied N is low with only 33% of the applied N ending up in grain.3 A major component of this is due to the poor uptake efficiency of cereals with only 40% of the applied N being taken up by the fertilized crop.4,5 N fertilizer use impacts on the environment in a number of ways. Greenhouse gas emissions from fertilizer production are substantial but emissions of nitrous oxides from unused N in soils are a major greenhouse gas contributor. Leaching and run off of N fertilizer leads to N pollution of groundwater, rivers, and oceans. This occurs not just in high rainfall regions, as well as early season waterlogging in dry regions can leach N out of the root zone. Worldwide, the best known negative effects of N pollution are associated with algal blooms and fish kills, but recent literature suggest that the dramatic increase in reactive N inputs to the biosphere are of a greater scale than the increase in human derived carbon dioxide inputs and may have similar or large-scale effects on the biosphere.6 In response to the known environmental impacts of excess N fertilization, European countries have introduced systems, so that farmers now must account for all applied N fertilizer.7

NUE DEFINED Nitrogen use efficiency (NUE) is a measure of how much applied N fertilizer is utilized by the plant. The simplest definitions reflect the amount of biomass or grain production per unit of N. The two main components of NUE are the efficiency with which N is taken up from the soil (uptake efficiency) and the efficiency with which N is converted to grain (utilization efficiency).8 Perhaps the most practically useful definition of NUE in this context is the physiological efficiency which takes into account the crop performance in comparison to an unfertilized control, or between two levels of N supply.9

Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00006-5 Copyright © 2017 Elsevier Inc. All rights reserved.

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STRATEGIES TO IMPROVE NUE INCREASING UPTAKE EFFICIENCY As discussed earlier, there is considerable scope for improvement in the N uptake efficiency (NUpE)4,5 and there are a number of ways this could be achieved.

Increasing uptake capacity

Increased NO3− uptake capacity may be achieved through better NO3− transporters, more effective regulation of the transport system, or better storage and assimilation. Increasing the uptake capacity of roots is not simple because of the tight regulation of N uptake, N taken up surplus to requirements increasing plant N status, which in turn leads to feedback regulation and reduction in uptake capacity.10 There is genetic variability in root N uptake capacity11–14 and it may be possible for this to be exploited by breeding programs or the information gained from understanding these differences can be used to direct the targeted manipulation of transport processes. Moose and Below15 point out that in maize, although yield per unit of N has increased over time in new hybrids through increased utilization efficiency, uptake per plant has stayed the same, implying that N uptake improvement by conventional breeding approaches has reached its limit. If this limitation is real, then it is justification of nonconventional approaches, such as those described later. However, these results contrast with Aziz et al.16 in wheat, who found that 50 years of breeding for yield has led to Australian wheat varieties with smaller roots but these roots have enhanced N uptake capacity.

Changing root morphology Due to the mobility of N in the soil, nutrient uptake modeling indicates that root morphology is of less ­relevance for N uptake than for nutrients, such as phosphorus.17,18 However, given that NO3− uptake is dependent on water movement root morphology that would have a greater impact on NO3− uptake in drying soils.19 Apart from drying soils, there are other circumstances where root morphology is of importance to N fertilizer uptake. In deep sands, NO3− is readily leached down the soil profile beyond recovery by crops. Plants with rapidly growing deep roots could prevent some of these losses and allow recovery of nutrient and water that would otherwise be inaccessible,20–22 however this comes at a cost of greater carbon allocation to roots. Stay green is a trait, whereby plants have delayed senescence allowing for a longer period of photosynthesis.23,24 In deep soils of the northern Australian wheat cropping zone this stay green characteristic has been related to deep rooted genotypes being able to extract water from deep in the soil profile.25 For sorghum and maize this trait was thought to be related to higher specific leaf N, allowing greater N and carbon allocation for root growth.26,27 Remobilization of N in stay green genotypes can be reduced with reduced senescence, a lower N harvest index, but this is thought to be offset by increased N uptake from the soil during grain filling.28 The usefulness of this trait may be limited to these deep soils, where crops rely on stored water for growth.

INCREASING UTILIZATION EFFICIENCY Modifying specific leaf N Increasing the photosynthetic N utilization efficiency (PsNUE) is another approach to improve NUE. By increasing the leaf area index and decreasing the specific leaf N, the radiation use efficiency could be increased.20 The key enzyme involved in carbon fixation is ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and this enzyme makes up a large proportion of leaf N.29 Rubisco is also involved in photorespiratory losses, which can be as high as 20% of total carbon fixation in C3 plants and also liberates ammonia

 Genetic approaches to improve NUE

95

which requires reassimilation.30 Increasing the efficiency of Rubisco should improve PsNUE and progress toward this goal has been made.31 In C4 plants, such as maize, photorespiration is limited by structural and metabolic changes making them much more efficient in terms of carbon and N.32 Increasing PsNUE may have a downside, since lower leaf N levels reduce the amount of N available for remobilization.

Delayed senescence (stay green) Stay green was introduced above in relation to uptake efficiency. Although the mechanisms are not fully understood, it appears that stay green is not necessarily linked to continued uptake of N and water by roots.33 Stay green can be linked to delayed senescence which could be beneficial by enabling continued photosynthesis with age but could be detrimental for NUE if it reduced remobilization of N to the grain.

Increasing remobilization efficiency

N remobilized from vegetative parts of the grain accounts for between 60% and 92% of grain N.34–37 The percentage of N remobilized to the grain is usually lower with high N supply8 but is also dependent on the extent of postanthesis N uptake and environment.26,34,36,38 The efficiency of N remobilization has been found to increase with water stress, likely linked to a reduced uptake of N from drying soils.35 The multitude of factors that affect remobilization makes it a difficult trait to select for. Improved N content in wheat grains related to the high grain protein content locus (Gpc-B1) was found to be due to early senescence regulated by a NAM (no apical meristem) transcription factor.39 For wheat, grain protein is an important determinant of the price a grower gets for their crop. An inverse relationship has been observed between grain protein and yield, with higher yielding crops commonly showing decreased grain protein.40 The nature of this relationship is not simple with the strength of the relationship varying considerably between studies and there being a significant genotype × environment interaction.41 Selecting genotypes which deviate from this negative relationship, those genotypes which maintain protein with high yield, has been suggested as a tool for breeding programs to address this issue.42

GENETIC APPROACHES TO IMPROVE NUE New crop varieties are predominantly selected in field trials, which have received large amounts of fertilizer, generally in accordance with the local agricultural practice. A reasonable conclusion would be that modern varieties have decreased N uptake and assimilation capacity over time simply because these traits were not intentionally selected for in-breeding programs. In fact, the opposite is true with several studies showing that wheat and barley varieties produced over the past century have steadily increasing N uptake and assimilation resulting in increased NUE.43–45 Thus, modern varieties outperform older ones even in trials conducted with low-N fertilizer application.13,46,47 However, it is still clear that this rate of NUE increase will not be sufficient to deal with increasing fertilizer and environmental costs while supplying sufficient grain production to support a growing population. Identification of important NUE-related genes may be accomplished using a genetic approach and has been recently reviewed for potential to improve NUE in wheat.48 However, as with other complex traits, such as drought tolerance, the current understanding of the genetic elements providing NUE in cereal, oilseed and legume crops is steadily growing, but quite limited when compared to other traits, such as disease resistance. NUE related genetic studies in wheat, maize, and rice are further progressed than other crops and genetic information gleaned from these studies may be incorporated into genetic strategies to improve NUE of other cereals, oilseeds, and legumes.

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CHAPTER 6  NITROGEN USE EFFICIENCY IN CROP PLANTS

Discovering quantitative trait loci (QTL) regulating NUE in crops will be beneficial on several levels.15 First, the loci regulating NUE and component subtraits will be identified. Second, next-generation map-based cloning technologies makes QTL mapping a viable approach for identifying and cloning genes with on the potential to improve NUE and component subtraits. Molecular markers that are tightly linked to NUE loci may be developed to enable selection of such NUE genes reducing the requirement to phenotype germplasm with multiple N treatments and multiple trial sites. Third, identification of NUE QTL will be crucial when crossing genes controlling improved NUE into multiple genetic backgrounds to ensure maximum trait expression and stability.

IDENTIFYING GENOTYPIC VARIATION FOR NUE The critical first step in any forward genetic and breeding approach is to identify genetic variation in available germplasm. NUE is a complex trait, thus involves measurement of a suite of component subtraits, which can vary in importance based on target environments and crops. Variation for NUE and related subtraits have been identified in wheat,12,37,49–51 barley,52–54 legumes,55 oilseeds,56–59 maize,60–62 rice,63–68 and the model plant species Arabidopsis.69,70 An increasing number of studies have produced data for relative grain yield between low and high-N treatments, one of the most relevant measurements for assessing NUE variation in trials. However, several of the aforementioned studies provide NUE measurements (e.g., grain yield per unit of available N) for the relevant set of varieties at low and high N treatments separately. For example, NUE (kg grain per kg available N) was measured in 10 field grown barley lines and the highest ranking lines had 20%–40% higher NUE than the lowest lines within individual N treatments and trial years.54 Similar results have been found in other crops, suggesting there is sufficient variation within varieties currently grown by producers for genetic mapping studies. It is important to note, however, these measurements can be problematic as NUE measurements and even rankings of the NUE of varieties are seldom repeatable from year-toyear. This suggests that environmental factors, such as rainfall, have important interactions with NUE in field trials and indicates future variety evaluations will need to be conducted over multiple years or be combined with trials in more carefully controlled conditions. Exotic germplasm may be useful in better understanding particular aspects of NUE and this knowledge can be utilized in designing breeding goals. New sources of genetic variation are being explored to expand the genetic potential to improve NUE and related traits in commercial varieties. For example, the Tibetan wild barley collection is being explored for useful variation in response to low N conditions,71,72 however these lines will need incorporation into advanced genetic populations prior to genetic mapping efforts to reduce artefactual data resulting from differences in phenology of the material.73 Maize breeding has successfully used this approach for a variety of traits and is being utilized in combination with association mapping studies,74 suggesting there is potential to use exotic germplasm in NUE genetic studies and improvement strategies in other crops.

DISCOVERING GENETIC LOCI FOR NUE NUE and relevant subtraits have been measured in a large number of mapping populations (both biparental and, more recently, association mapping collections), and these studies are summarized in Table 6.1. Wheat and maize have particular populations that have been most extensively studied, where the parental genotypes had significant differences for NUE. The wheat biparental RIL population Arche × Recital

Table 6.1  Summary of Studies That Have Identified Genetic Loci for Traits Associated With NUE Variations Explained (%)

References

Crops

Population

Environments

Arabidopsis thaliana

Col-0 × Ler

Glasshouse

99 RIL

High

Root length, aerial mass, root mass on 3 N types

19

N/A

[84]

Bay-0 × Shah

Glasshouse

415 RIL

Low, high

Water, NO3− , chloride, PO −4 content

42

2.0–21.0

[85]

Bay-0 × Shah

Glasshouse

415 RIL

Low, high

Shoot dry matter, total N/NO3− /amino acids

84

2.0–21.0

[86]

Lewis × Karl

Field

146 RIL

Normal

Tissue N/ protein contents, agronomic traits, yield

51

5.8–45.9

[87]

Blenheim × Kym

Field

99 DH

Normal

Grain carbohydrate/N

16

N/A

[88]

H. vulgare ssp sponteneum × Scarlett

Field

301 DH

Low, high

Agronomic and yield traits

82

0.1–56.1

[89]

Lewis × Karl

Field

146 RIL

Normal

Peptidase activities

20

8.4–20.8

[90]

F2 × Io

Field

218 RIL

Low, high

Agronomic, yield and N-related traits

608

3.5–19.5

[91]

B73 × G79

Field

214 RIL

Very low, high

Agronomic and yield traits

44

7.1–18.1

[92]

F2 × Io

Field

99 RIL

Low, High

Agronomic and yield traits

29

7.0–48.0a

[79]

Barley (Hordeum vulgare)

Maize (Zea mays)

N Treatments

Loci Numbers

Population Sizes

Traits

(Continued)

Table 6.1  Summary of Studies That Have Identified Genetic Loci for Traits Associated With NUE (cont.)

Crops

Population

Environments

Population Sizes

N Treatments

Loci Numbers

Traits − 3

Variations Explained (%)

References

a

Glasshouse

77 RIL

Low

Leaf NO content, NR and GS activities

13

28.0–52.0

Field

99 RIL

Low, high

Tissue N content, yield components, N remobilisation

67

9.2–16.4

Glasshouse

77 RIL

Low

Leaf NO3− content, NR, GS and GDH activities

16

28.0–52.5a

B73 ×  Mo17

Glasshouse

94 IRIL

Normal

10 Leaf enzyme activities, shoot biomass

88

3.4–24.2

[93]

F2 × Io

Field

99 RIL

Low, high

Agronomic and 62 yield component traits

9.0–23.3

[81]

Ac7643S5 × Ac7729/ TZSRWS5

Field

240 F2:3

Low, high

Agronomic and yield components

72

0.4–21.3

[94]

Z3 × 87-1

Glasshouse

94 RIL

Low, high

Root traits

23

11.0–43.7

[95]

F2 × Io

[80]

a

F2 × Io

Glasshouse

140 RIL

N/A

Seed germination and GS activity

9

7.3–18.2

[83]

F2 × Io

Field

53 RIL

Low, high

Tissue 15N content

33

9.0–21.9

[82]

Huang-C × Xu178

Field

213 F2:3

Low, high

Stover nutrient/ fiber/protein content

28

7.4–23.6

[96]

Variations Explained (%)

References

Population

Environments

Ye478 × Wu312

Field

218 RIL

Low, normal

Grain yield, leaf traits, flowering time, anthesis– silking interval

61

5.2–21.9

[97]

F2 × Io

Field

100 RIL

Normal

Kernel and cob enzyme activities and amino acids

33

9.0–31.0

[98]

Ye478 × 9 inbred lines

Field

74 RIL

Low, normal

Yield and yield components

42

4.0–29.2

[99]

Medicago (Medicago truncatula)

Jemalong A17 × DZA315.16

Glasshouse

175 RIL

Low, high

Biomass, tissue N, calculated efficiency variables

34

6.0–52.0

[100]

Pea (Pisum sativum)

Terese × K586

Field

139 RIL

Low

Agronomic and yield traits, tissue N content

117

8.0–90.0

[101]

Cameor × Ballet

Both

207 RIL

Low

Root/nodule traits, N accumulation/ content traits

178

4.6–45.1

[102]

93-11 × Nipponbare

Hydroponic

119 RIL

Low, high

Plant height, root length, DW

44

5.5–33.2

[103]

Zhenshan 97 × Minggui 63

Field

127 RIL

Low, normal

Grain yield, biomass, tissue N, calculated efficiency

30

4.0–16.6

[104]

Zhenshan 97 × HR95

Field

188 RIL

Low, medium, high

Grain yield and 57 component traits

0.8–23.1

[105]

Zhenshan97 × Minghui63

Glasshouse

239 RIL

Very low, Normal

Root/shoot/plant weight; relative weight

52

1.2–17.5

[106]

R9308 x Xieqingzao B

Glasshouse

238 RIL

Low, normal

Relative DW, root length, shoot height

7

9.1–14.5

[107]

Crops

Rice (Oryza sativa)

N Treatments

Loci Numbers

Population Sizes

Traits

(Continued)

Table 6.1  Summary of Studies That Have Identified Genetic Loci for Traits Associated With NUE (cont.) Variations Explained (%)

References

13

4.4–7.7

[108]

Population

Environments

Nipponbare × Kasalath

Glasshouse

98 RIL

Normal

Leaf GS and GOGAT protein content

Dasanbyeo × TR22183

Field

166 RIL

Low, normal

Tissue N 78 content, yield component traits

6.9–32.1

[109]

IR64 × Azucena

Glasshouse

82 DH

Low, normal, high

Agronomic and 16 yield component traits

15.7–50.3

[110]

Koshihikari × Kasalath

Glasshouse

38 CSSL

Low, high

Seminal root length

9

N/A

[111]

Taichung 65 × IRGC 104038

Glasshouse

161 RIL

Low, high

Seminal root length

8

7.5–19.4

[112]

Sugarcane (Saccharum officinarum)

Q165 × IJ76-514

Glasshouse

168 F1

Low, high

DW, tissue N, GS activity, protein, internal NUE

281

3.0–19.0

[113]

Wheat (Triticum aestivum)

C-FGRC Core collection

Field

196 Accessions

Low, high

Yield, yield components, GPC

54

4.4–25.3

[114]

Elite European varieties

Field

225 Varieties Low, high

28 NUE-related traits

333

0.2–12.9

[115]

Crops

Chuan 35050 × Shannong Hydroponic 483

N Treatments

Loci Numbers

Population Sizes

Traits

131 RIL

3NO3− / NH+4 ratios

Seedling biomass

51

7.0–55.6

[116]

Arche × Recital

Field

222 DH

Low, optimal

Yield, yield components, nitrogen nutrition index

45

4.6–32.6

[117]

Arche × Recital

Field

222 DH

Low, high

Yield, thousand kernel weight

63

4.6–26.8

[118]

Crops

Population Sizes

N Treatments

Traits

Loci Numbers

Variations Explained (%)

References

Population

Environments

Toisondor × Quebon CF9107 × Quebon Toisondor × CF9107

Field

230 DH 316 DH 143 DH

Low, high

Yield, GPC

89

10.0–59.0

[119]

Toisondor × CF9107

Field

140 DH

Low, high

Yield, GPC

104

2.0–47.0

[120]

Kenong 9204 × Jing 411

Field

188 RIL

Low, high

GPC, grain quality traits

158

2.9–35.6

[121]

RAC875 × Kukri

Field

156 DH

Low, medium, high

Yield, GPC

25

6.0–20.0

[122]

Hanxuan10 × Lumai14

Field and hydroponics

120 DH

Low, high

N uptake, DW, Total N

34

4.3–21.9

[123]

Chinese Spring × SQ1

Glasshouse

95 DH

Normal

Leaf enzyme activities, agronomic traits, Tissue N

164

2.7–30.5

[124]

Arche × Recital

Field

241 DH

Low, high

Yield, agronomic traits, N/protein content

79

3.3–33.0

[76]

3 Populations above

In silico

N/A

N/A

Meta QTL

11

N/A

[125]

Arche × Recital

Field

241 DH

Normal

Leaf enzyme activities, amino acids, N/ protein content, agronomic traits

155

4.8–31.9

[77]

Arche × Recital

Glasshouse

120 DH

Low

Root architecture traits, DW

32

8.6–39.0

[75]

Langdon × DIC6B

Field and glasshouse

N/A

N/A

Grain protein, zinc, iron content and senescence

1

NAC gene identifiedb

[39]

(Continued)

Table 6.1  Summary of Studies That Have Identified Genetic Loci for Traits Associated With NUE (cont.)

Crops

Variations Explained (%)

References

Population

Environments

Xiaoyan 54 × Jing 411

Field

182 RIL

Low, normal

Grain yield, yield components, tissue N, calculated efficiency parameters

117

1.6–35.2

[126]

131 RIL

Low, middle, high

Root traits, DW, tissue N, calculated NUE

197

6.2–27.3

[127]

Chuan 35050 × Shannong Hydroponic 483

N Treatments

Loci Numbers

Population Sizes

Traits

CSSL, Chromosome segment substitution lines; DH, doubled haploid; DW, dry weight; GDH, glutamate dehydrogenase; GOGAT, glutamate synthase; GS, glutamine synthetase; IRIL, intermated recombinant inbred line; NR, nitrate reductase; RIL, recombinant inbred line. a Total variation explained by all QTL detected for a trait. b QTL was identified in earlier studies in multiple environments and populations, the QTL was fine mapped and a single NAC gene was identified.

 Genetic approaches to improve NUE

103

was phenotyped for a large variety of NUE-related traits in glasshouse and field studies.75–77 Similarly, the maize biparental RIL population F2 × Io population has been used for multiple NUE-related studies and the QTL for agronomic traits identified in older studies have been compared to newly identified QTL for biochemical traits to search for colocalization of traits.78–83 This approach maximizes the data extractable from individual populations, however it remains to be seen how relevant the identified loci are for regulating NUE in separate populations or different target environments. Attempts to identify candidate genes located beneath QTL have often revealed genes which control photoperiod (Ppd-A1 and Ppd-B1), dwarfing (Rht-B1 and Rht-12), and vernalization (Vrn-A1 and Vrn-D1). This factor needs to be addressed in the genetic analysis of the phenotypic data collected for a particular population. A meta-QTL analysis analyzed data from three previous mapping studies in wheat and showed these candidate genes were located under the 11 meta-QTL identified.125 This is not surprising, since these traits control developmental time, thus affecting the time the crop has for N uptake and assimilation.73 Genetic loci putatively regulating total N uptake have been identified in wheat and maize.82,123 ­Candidate genes have not been identified for N uptake based on these genetic analyses and it will be interesting to see if such candidates are related to plant size, root growth, or to actual unidirectional N uptake capacity, all of which are potential target traits to improve N uptake capacity.128 Another class of candidate genes discussed in several mapping studies are those encoding enzymes related to the assimilation and remobilization of N. Genetic analysis in barley has identified a locus on chromosome 6 which regulates N reallocation and grain protein content (GPC).129–131 The causal gene has not been identified, however it is a reasonable assumption that a known N assimilation enzyme may be the can­ didate. A significant proportion of QTL identified as regulating GPC across species have genes encoding glutamine synthetase (GS), glutamate synthase (GOGAT) or NO3− reductase (NR) located within the QTL interval.132–135 This provides further evidence that these genes are important for NUE and may be suitable targets for further breeding and transgenic approaches to improving NUE.136 It should be noted, however, the QTL intervals containing these genes are quite often extremely wide with small effects, thus further fine mapping studies and ultimately transgenic manipulation of the genes would be required to strengthen this hypothesis. The QTL regulating activity of several N assimilation enzymes in the maize B73 × Mo17 IBM population were identified and only 3 of 81 identified QTL were cis-QTL, meaning the gene encoding the relevant enzyme was located beneath the activity QTL.93 A similar study in a maize NAM population identified multiple genetic loci regulating N metabolite levels in leaf tissue, however few of the genes had previously established associations with the metabolite.137 This suggests that genes encoding proteins regulating the activity of the N assimilation or remobilization enzyme may be more important targets than the gene encoding the enzyme itself for improving NUE through breeding and transgenic approaches. Perhaps the most common definition of NUE involves a relative value for measurements of subtraits or yield at low versus high N supply (physiological NUE). However, there are few studies that have identified QTL for this type of relative measurement. A large proportion of the NUE QTL studies summarized in Table 6.1 were undertaken at a single N level or with a combination of “normal” N provision and “no” N provision. This limits the utility of individual studies and creates problems in assembling genetic data between studies as, for example, European soils without added fertilizer often contain N levels comparable to those found in fertilized Australian soils. Two studies from rice mapped QTL for NUE by producing relative phenotypic values between low and high N provision106,107 and more of this type of experiment would be useful to identify QTL for NUE. This type of data is not easy to collect,

104

CHAPTER 6  NITROGEN USE EFFICIENCY IN CROP PLANTS

since the inherent difficulty in accurately measuring NUE traits in the glasshouse and field can be magnified when comparing relative trait values at low and high N rendering the relative values (and their associated QTL) prone to large errors. Indeed, most of the studies summarized in Table 6.1 identified large numbers of QTL explaining small percentages of the variation for each measured trait and often these QTL do not appear in multiple studies. Similarly, the majority of the QTL reported do not appear from one year to the next within a published study. This may indicate that difficulty involved in the measurement of NUE traits, however more likely it indicates that NUE traits are variable due to interactions with other factors, such as available water, temperature, or disease pressure. Clearly, new phenotyping techniques and equipment are required to control for the interacting factors. Results produced in glasshouse phenotyping, or with a combination of rainout shelters and irrigation facilities in field trials may be useful in this regard to understand genetic data produced in more uncontrolled field trials. Accurate phenotyping data, in addition to quality genetic resources, permit reproducibility of QTL mapping, the opportunity to fine map QTL, and finally the cloning of the candidate gene. As such, very few genes have been identified as candidates responsible for NUE QTL. One example is the NAC transcription factor gene discovered in a population derived from a cross between wild emmer wheat and durum. The transcription factor regulates apparent pleiotropic traits, including senescence and grain protein, zinc, and iron content.39 The authors speculated the increased content is due to increased remobilization of N, zinc, and iron to the grain, however further analysis is required to determine if this is simply related to delayed senescence allowing increased time for nutrient uptake and assimilation. More recently, NRT1.1B, a gene encoding a low-affinity NO3− transporter in rice, was identified by fine mapping a QTL regulating N uptake in a population created between indica and japonica subspecies, which differ in N uptake capacity.138 Incorporation of the indica allele into the japonica background through both introgression and transgenic methods improved N uptake and NUE, indicating improving N uptake capacity may be an important strategy for other crop species. However, these studies show the potential in using a genetic approach to improve NUE in crops and identify candidate genes. Further, this approach allows development of perfect molecular markers for important NUE genes in breeding programs, as well as the cloning of these genes for introduction into other germplasm through ­transgenic approaches.

IMPROVING CROP NUE USING GENETIC INFORMATION Current QTL data are of limited use for improving NUE in new crop varieties. This is because it is a complex trait with a large number of QTL with little overlap between studies, and also because of significant interactions with the target environment, which can vary significantly between cropping seasons. It is likely a dissection of the genetics of NUE relevant to a particular cropping zone using mapping populations with material adapted to the particular zone will be required. Progress in this may be enhanced by carrying out mapping studies in controlled environments in parallel with field-based studies. Along with developing high-throughput phenotyping methods for assessing traits related to NUE in varieties and populations suitable for specific cropping environments, efforts to improve NUE in crop plants will need to be supported by developments and improvements in platform technologies, including bioinformatics and biotechnology, particularly in crops with complex genomes, such as bread wheat. Collins et al.139 identified several factors which will speed the identification and cloning of QTL. First, improvement of molecular platforms, such as single nucleotide polymorphism profiling, “omics”

 Transgenic approaches to improve NUE

105

profiling, tiling arrays, and mapping approaches, such as association mapping. Second, development of new types of genetic populations and approaches, including multiparental approaches and advanced backcross mapping populations using exotic germplasm, such as the multiparent advanced generation intercross (MAGIC) or nested association mapping (NAM) populations available in barley.140,141 Next, progress in bioinformatics will improve functional maps and underpin efforts in comparative mapping. Finally, sequencing technology advances (genome, transcriptome, etc.) and tools for functionally characterising genes, such as targeting induced local lesions in genomes (TILLING) and RNA interference will decrease time from identification of QTL to cloning of candidate genes.

TRANSGENIC APPROACHES TO IMPROVE NUE One of the most common molecular genetic approaches to increase NUE is direct gene manipulation by biotechnology techniques, which include transgenics, mutagenesis, and genome editing, such as the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system.142 The transgenic approach has been most widely used because it is a highly versatile method, allowing researchers to introduce genes of interest in a semicontrolled manner, by combining with a specific promoter (i.e., tissue specific, stress inducible, or ectopic).143 Genetically modified (GM) crops have been developed with this approach, and commercial releases of new varieties are mainly altered for agronomic traits, such as disease, pest, or herbicide resistance to date. Enhancing NUE via biotechnological approaches has been a key research and commercial interest more than a decade. There are a few reviews of the topic providing comprehensive summaries,9,143,144 however in the following sections we update the recent progresses in the area, and touch on the prospective directions.

TARGETED APPROACH TO IMPROVE NUE As mentioned earlier the processes combining to determine NUE can be divided into components of N acquisition, assimilation, and their regulation, thus it is sensible to classify the research outcomes in the same manner. Table 6.2 shows the summary of the biotechnological attempts to enhance N-related phenotypes. The immediately obvious favored approach to increase N acquisition is by manipulating N transporters. Although transgenic studies with N transporters were successful in terms of verifying their functions,145–147 there were few reports which showed improved N uptake and NUE in the transgenic lines until recently.148 An early study by Fraiser et al.147 overexpressed the high-affinity nitrate transporter NRT2.1 in tobacco, and found that the transgenic lines absorbed more nitrate than wild-type plants when they were grown under high external nitrate concentration. More recently, the rice NRT2.1 was expressed under the NAR2.1 promoter, and the transgenic rice lines showed increased biomass and grain yield.149 Similarly, Fan et al.150 generated NRT2.3b overexpression lines in rice, which is one of two splice variants of the NRT2.3 nitrate transporter and also operates as a pH sensor. N uptake, NUE, and grain yield increased up to 40% in the transgenic lines under field conditions.150 There were also increases in N uptake with manipulation of other transporters, including the ammonium transporter AMT1, the peptide transporter PTR6, and the urea transporter DUR3.151–153 Nitrate assimilation is initiated by nitrate reductase (NR) followed by nitrite reductase (NiR) which are both well regulated by physiological and environmental factors, such as N and C status in the cells, and light/dark period. Overexpression of these enzyme genes often alters metabolic

Table 6.2  Biotech Approaches for NUE-Related Traits Transgenes

Functions

Transgene Sources Promoters

Recipient Species

Transgenic Phenotypes

References

NRT1.1(CHL1/ NPF6.3)

Nitrate transporter

A. thaliana

CaMV 35S

A. thaliana

Nitrate uptake

[145]

NRT2.1

Nitrate transporter

N. plumbaginifolia

CaMV 35S rolD

Tobacco (N. tabacum)

Nitrate uptake, content

[147]

NRT2.1

Nitrate transporter

Rice (O. sativa)

OsNar2.1

Rice (O. sativa)

Increased biomass and grain yield

[149]

NRT2.3b

Nitrate transporter pH sensor

Rice (O. sativa)

CaMV 35S

Rice (O. sativa)

Improved N uptake and NUE Increased grain yield

[150]

NRT3.1(NAR2.1)

Nitrate transport component

A. thaliana

CaMV 35S

A. thaliana nrt3.1 mutants

Nitrate uptake

[146]

AMT1.1

Ammonium transporter

Rice (O. sativa)

Ubiquitin

Rice (O. sativa, ssp. japonica)

Increased ammonium uptake

[153]

AMT1.1

Ammonium transporter

Rice (O. sativa)

Ubiquitin

Rice (O. sativa)

Increased ammonium uptake, seed yield

[154]

DUR3

Urea transporter

Rice (O. sativa)

CaMV 35S

A. thaliana

Increased urea uptake

[152]

PTR6(NPF7.3)

Peptide transporter

Rice (O. sativa)

Ubiquitin

Rice (O. sativa)

Increased plant height, biomass

[151]

Nia1 (NR1)

Nitrate reductase

Tobacco (Nicotiana tabacum) (Ser521 mutation)

CaMV 35S

N. plumbaginifolia

NR activity, nitrate accumulation

[155]

Nia1 (NR1)

Nitrate reductase

Tobacco (N. tabacum)

CaMV 35S

Wheat (T. aestivum)

Increased biomass, and grain yield

[156]

Nia2(NR2)

Nitrate reductase

Tobacco (N. tabacum)

CaMV 35S

Potato (Solanum tuberosum)

Reduced nitrate levels

[157,158]

NiR

Nitrite reductase

Tobacco (N. tabacum)

CaMV 35S

Tobacco (N. tabacum), A. thaliana

NiR activity

[159]

N acquisition

N assimilation

Transgenes

Functions

Transgene Sources Promoters

Recipient Species

Transgenic Phenotypes

References

NiR

Nitrite reductase

Spinach (Spinacia oleracea)

CaMV 35S

A. thaliana

NO2 assimilation

[160]

NiR

Nitrite reductase

A. thaliana

CERV

Tobacco (N. tabacum)

Increased chlorophyll Stay-green

[161]

GS1

Glutamine synthetase (cytosolic)

Soybean (Glycine max)

CaMV 35S

Lotus japonicus

Decreased ammonium uptake

[162]

GS1

Glutamine synthetase (cytosolic)

Bean (Phaseolus vulgaris)

rbcS

Wheat (T. aestivum)

Increased N uptake

[163]

GS1

Glutamine synthetase (cytosolic)

Tobacco (N. tabacum)

CaMV 35S

Tobacco (N. tabacum)

Increased biomass

[164]

GS1

Glutamine synthetase (cytosolic)

Pea (P. sativum)

CaMV 35S

Tobacco (N. tabacum)

Increased biomass, protein

[165]

GS1

Glutamine synthetase (cytosolic)

Soybean (G. max)

CaMV 35S, LBC3 (nodule), rolD (root)

Pea (P. sativum)

Higher GS activity in some lines

[166]

GS1

Glutamine synthetase (cytosolic)

Pine (Pinus sylvestris)

CaMV 35S

Poplar (Populus spp.)

Increased biomass

[167]

GS1

Glutamine synthetase (cytosolic)

Soybean (G. max)

LBC3 (nodule), rolD (root)

Pea (P. sativum)

Increased biomass in some rolD lines

[168]

GS1

Glutamine synthetase (cytosolic)

Maize (Z. mays)

pCsVMV (OX)

Maize (Z. mays)

Increased kernel number

[169]

GS1

Glutamine synthetase (cytosolic)

Rice (O. sativa)

CaMV 35S

Rice (O. sativa)

Increased N, decreased seed yield

[170]

GS1

Glutamine synthetase (cytosolic)

Rice (O. sativa)

Ubiquitin

Rice (O. sativa)

Enhanced NHI and UtE in controlled environment

[144]

GS1

Glutamine synthetase (cytosolic)

A. thaliana

PrbcS

Tobacco (N. tabacum)

Enhanced N assimilation under low N

[171]

GS1

Glutamine synthetase (cytosolic)

Rice (O. sativa)

CaMV 35S

Rice (O. sativa)

Inhibited growth

[172] (Continued)

Table 6.2  Biotech Approaches for NUE-Related Traits (cont.) Transgenes

Functions

Transgene Sources Promoters

Recipient Species

Transgenic Phenotypes

References

GS1

Glutamine synthetase (cytosolic)

Dunaliellaviridis (alga)

CaMV 35S

A. thaliana

Improved biomass

[173]

GS1

Glutamine synthetase(cytosolic)

Klebsiella

CaMV 35S

A. thaliana

Increased biomass, NUE

[174]

GS1

Glutamine synthetase (cytosolic)

Lactococcus

CaMV 35S

A. thaliana

Increased biomass, NUE

[174]

GS1

Glutamine synthetase (cytosolic)

Sorghum (Sorghum bicolor)

Ubiquitin

Sorghum (Sorghum bicolor)

Increased tiller number and biomass

[175]

GS2

Glutamine synthetase (plastidic)

Rice (O. sativa)

CaMV 35S

Rice (O. sativa)

Photorespiration capacity up

[176]

GS2

Glutamine synthetase (plastidic)

Tobacco (N. tabacum)

Soybean Rubisco

Tobacco (N. tabacum)

Increased growth rate

[177]

GS2

Glutamine synthetase (plastidic)

A. thaliana

PrbcS

Tobacco (N. tabacum)

Enhanced N assimilation under low N

[171]

GOGAT

Glutamate synthase

Tobacco (N. tabacum)

CaMV 35S

Tobacco (N. tabacum)

Increased biomass

[178]

GOGAT

Glutamate synthase

Rice (O. sativa ssp. japonica)

GOGAT (japonica rice)

Rice (O. sativa ssp. indica)

Increased grain weight

[179]

AlaAT

Alanine aminotransferase

Barley (H. vulgare)

BnBtg26

Canola (Brassica napus)

Increased biomass and seed yield

[180]

AlaAT

Alanine aminotransferase

Barley (H. vulgare)

OsAnt1

Rice (O. sativa)

Increased biomass and seed yield

[181]

AlaAT

Alanine aminotransferase

Barley (H. vulgare)

OsAnt1

Sugarcane (S. officinarum)

Improved NUE, biomass

[182]

AlaAT

Alanine aminotransferase

A. thaliana

OsAnt1

A. thaliana

Increased root length, shoot area

[183]

AlaAT

Alanine aminotransferase

A. thaliana

CaMV 35S

A. thaliana

Increased root length, shoot area

[183]

AlaAT

Alanine aminotransferase

Mus muscus

CaMV 35S

A. thaliana

Increased root length, shoot area

[183]

Transgenes

Functions

Transgene Sources Promoters

Recipient Species

Transgenic Phenotypes

References

AlaAT

Alanine aminotransferase

Pyrococcusfuriosus

CaMV 35S

A. thaliana

Increased root length, shoot area

[183]

AspAT

Asparatate aminotransferase

Panicummiliaceum

CaMV 35S

Tobacco (N. tabacum)

Enzyme activity, PEPC activity

[184]

GDHA

Glutamate dehydrogenase

E.coli

CaMV 35S

Tobacco (N. tabacum)

Increased plant biomass, DW, yield in field

[185]

GDHA

Glutamate dehydrogenase

Aspergillus

CaMV 35S

Rice (O. sativa)

Increased DW, N, yield in field

[186]

GDHA

Glutamate dehydrogenase

Aspergillus

CaMV 35S

Potato (Solanum tuberosum)

Increased tuber production

[187]

ASN1

Asparagine synthetase

A. thaliana

CaMV 35S

A. thaliana

Enhanced N status in seeds

[188]

N regulatory pathway BT

Bric-a-brac/Tramtrack/ Broad (BTB) scaffold protein

T-DNA

N/A

A. thaliana mutant Rice (O. sativa) mutant

Increased nitrate uptake Improved NUE, seed production

[189]

DOF1

Transcription factor

Maize (Z. mays)

CaMV 35S

A. thaliana

N content 30% up Growth rate up under low N

[190]

DOF1

Transcription factor

Maize (Z. mays)

Ubiquitin

Rice (O. sativa)

Increased N, biomass under low N

[191]

DOF1

Transcription factor

A. thaliana

PrbcS

Tobacco (N. tabacum)

Enhanced N assmilation under low N

[171]

ENOD93-1

Early nodulin

Rice (O. sativa)

Ubiquitin

Rice (O. sativa)

Increased shoot biomass and seed yield

[192]

NAC2-5A

Transcription factor

Wheat (T. aestivum)

Ubiquitin

Wheat (T. aestivum)

Increased N uptake, grain yield

[193]

NLP7

Nodule inception-like protein

A. thaliana

CaMV 35S

A. thaliana Rice (O. sativa)

Increased plant biomass under low, sufficient N

[194]

ATG8

Lipid-conjugated ubiquitine-like protein

Soybean (G. max)

CaMV 35S

A. thaliana

Increased biomass, seed

[195]

AVP1D

Vacuolarpyrophosphatase

A. thaliana

CaMV 35S

Lettuce (Lctuca sativa)

Improved N uptake, biomass

[196]

Miscellaneous

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CHAPTER 6  NITROGEN USE EFFICIENCY IN CROP PLANTS

profiles, but has not shown an effect on biomass or NUE improvement. In Nicotiana plumbaginifolia transgenic plants expressing NR from tobacco, NR activity was increased, resulting in accumulation of nitrate.155 Glutamine synthetase (GS) is probably the most intensively studied gene in terms of NUE enhancement both in monocot and dicot plants (Table 6.2). In some cases transgenic lines overexpressing GS1 displayed improvement in NUE, increased biomass and higher grain yield.164,165,167,175 Conversely, there are reports of overexpression of GS genes in plants having no impact on productivity or only minor improvements in NUE despite the absence of modifications in metabolites or GS enzyme activity.162,166,170,172,197 The inconsistencies may be due to nonoptimized selection of the transgene source and promoter. Expression of GS genes from alga and bacteria in Arabidopsis also increased biomass and NUE, providing evidence that superior GS alleles must be identified to maximize improvements in NUE using this approach.173,174 Further fine-tuning of the promoter–gene combination may improve its stability and adaptability to various environmental factors. When glutamate dehydrogenase (GDH) from Escherichia coli and Aspergillus were transformed into tobacco and rice plants, respectively, the transgenic plants showed improvement in biomass and seed yields.185,186 The transgenic rice lines also showed enhanced GS-GOGAT pathway activity, indicating this approach may provide an improvement in ammonium assimilation, thereby increasing crop production.186 Transgenic potato plants, expressing Aspergillus GDH, also displayed improved photosynthesis and NUE, resulting in increased tuber production.187 The alanine aminotransferase (AlaAT) NUE technology is probably the most advanced biotech approach in a commercial context for improving NUE. Barley AlaAT was transformed to canola and rice under the promoters of the antiquitin genes btg26 and OsAnt1, respectively. In both cases transgenic plants showed improved NUE phenotypes, increased biomass, and higher grain yield compared to control plants.180,181 The transgenic canola lines required 40% less N fertilizer than wildtype plants to produce the same high yield in field trials.180 Similarly, the rice transgenic lines required 12% less N fertilizer than wildtype plants to produce the same yield in controlled environment studies.181 The gene combination of OsAnt1::HvAlaAT was also examined in sugarcane and some transgenic lines displayed increased biomass production and NUE compared to the untransformed wildtype plants in a glasshouse trial.182 In a search for superior alleles to the barley AlaAT, McAllister, and Good183 expressed AlaAT homologs from mouse and bacteria in Arabidopsis, and the transgenic plants had increased root length and larger leaf areas. The biological mechanism behind the technology is poorly understood and needs to be elucidated in order to apply this approach to other species and/or improve performance even further. Although nitrate is a major inorganic N nutrient for plants, it also has a role as a signal to trigger the gene networks involved in its uptake and assimilation, but also provides developmental cues. Key regulatory hubs of the gene networks have been identified by mutant screens or transcriptomic analyses. Among those a few genes were successfully applied to improve NUE. One of the earliest examples is the DNA binding with one finger1 (DOF1) transcription factor. Yanagisawa et al.190 transformed maize DOF1 into Arabidopsis and the transgenic lines displayed modified N and C metabolism and improved growth under low N conditions. Similarly, improved NUE phenotypes were observed in rice lines expressing DOF1.191 Wang et al.171 produced transgenic tobacco plants in which DOF1 and two GS genes (i.e., GS1 and GS2) were overexpressed in a gene stack. The transgenic tobacco plants displayed improved N assimilation and sugar content, resulting in larger plants under low N. Interestingly, the tobacco plants had increased nitrate reductase (NR), phosphoenolpyruvate carboxylase (PEPC), and pyruvate kinase (PK) enzyme activities, as well as increased

 Transgenic approaches to improve NUE

111

GS activity.171 The success of this transgene combination might have resulted from a synergetic effect and will perhaps encourage subsequent attempts to improve NUE through the gene pyramiding approach.198 OsENOD93-1, known as an early nodulation gene, was transformed into rice and the overexpression lines displayed increased seed yield and improved NUE under low N condition.192 Another nodule related gene, NLP7 (NIN-Like Protein 7), was overexpressed in Arabidopsis and transgenic lines showed increased biomass under low and sufficient N.194 A negative regulator for nitrate uptake, the BT gene, a member of the Bric-a-Brac/Tramtrack/Broad gene family, was identified by gene network analysis. Overexpression of BT genes in Arabidopsis decreased NUE, whereas Arabidopsis and rice knockout mutants of the gene showed improved nitrate uptake and an increase of NUE by 65% and 20%, respectively.189 A nitrate inducible transcription factor NAC2-5A from wheat was overexpressed and the transgenic plants showed enhanced root growth and nitrate uptake, resulting in increased grain yield and N content.193 Although the original experimental aim was not to increase NUE, genes involved in plant size determination appear to play a role in determining N usage. Overexpression lines of Arabidopsis Vacuolar Pyrophosphatase1 (AVP1) resulted in plants with larger leaves,199 indicating the transgenic plants has enhanced NUE. Not surprisingly, when AVP1 was overexpressed in lettuce, NRT2.1 was upregulated, resulting in enhanced N uptake and biomass.196 ATG8, an AuTophaGy-related gene in soybean, was transformed into Arabidopsis under 35S promoter and the overexpression lines showed seed increases of 12.9% and larger shoot biomass under optimal or lower N conditions than wildtype.195 These may be ideal candidates for further examination in other crops to verify the benefits of the transgenes to the NUE-related traits.

IMPROVEMENT OF THE BIOTECH APPROACHES It is a standard approach to choose ectopic promoters, such as CMV35S and ubiquitin to drive a gene of interest (Table 6.2). However, overexpressing the gene(s) of interest may not be necessarily the best choice for altering targeted trait(s) due to possible negative effects such as cosuppression of the gene(s).200,201 Alternatively, depending on specific hypotheses on gene function, promoters can be chosen based on criteria, such as tissue-specific expression, inducibility triggered by environmental queues, and/or stable expression across generations. Even if a promising phenotype is identified in transgenic lines, it is still a long and cost/resource intensive processes to release GM crops in the market.202,203 There are some approaches which may accelerate the processes. Deregulation of GM crops is an undeniable hurdle because of requirements of regulatory agencies resulting in an enormous amount of commercialization costs. Furthermore, consumers’ acceptance of GM crops stays low.202,203 Cisgenesis is a technique to modify a gene of interest in a host plant with a native gene from a sexually compatible species. The US Environment Protection Agency (EPA) is considering declaring cisgenic plants to be exempt from the regulatory process, although it may still be case-by-case basis.204,205 European Food Safety Authority (EFSA) released a note stating undesired effects from cisgenic plants will be regulated in a similar way to conventionally bred plants.206 Genome editing tools are becoming available for crop breeding programs. CRISPR-Cas9 technology has been particularly implemented in crop plants to improve yield. Due to the nature of the technology, CRISPR-Cas9 may lower the hurdle of the deregulation steps.207

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CHAPTER 6  NITROGEN USE EFFICIENCY IN CROP PLANTS

FUTURE PROSPECTS Better phenotyping is a high priority both for understanding the genetics of NUE and evaluating new material. Because of the major effect of environment on N availability and demand, this is a major factor confounding NUE trial results. Aside from rainfall, soil characteristics are another important influence on N availability and trial outcomes. Better characterization of environmental data of field trials in general and within trial variation in environment will improve the quality of results. However, advances in plant growth analysis and imaging capacity have enabled the development of high-throughput methods to evaluate germplasm in a highly accurate and repeatable manner both in the field and in controlled environments.208 Protocols will need to be developed to evaluate germplasm for NUE traits alone and their interactions with other environmental aspects (genotype × nitrogen × environment—G × N × E). Ideally, the treatments should also include N management, that is, if split N applications are routine then this needs to be incorporated into trial designs. The ideal measure of NUE of a variety will be relative yield at low and adequate N provision, but also include component traits (e.g., biomass at a defined point in the lifecycle). Controlled environments, although not equivalent to the field, do allow better dissection of physiological and environmental components to traits, and also allow evaluation of transgenics where field measurements are often made difficult due to regulatory constraints or where seed quantity is limited, such as with newly developed populations. Phenotyping root traits may assist in the improvement of N uptake,209 however, above ground traits are easier to measure and have been the main target. There are examples of this approach in maize95 and pea,102 however, further development in root imaging technologies will make this approach more feasible. Existing mapping populations, or those developed, based on evaluation of NUE in existing varieties can be utilized for QTL and association mapping approaches. To date, association mapping has not been utilized in NUE trait mapping studies in cereals and represents an important resource that should be utilized in future work with existing varieties and diverse germplasm projects. Ultimately the genes responsible for the NUE locus will be identified through fine mapping and cloning, however beneficial NUE alleles may be moved into existing varieties using marker assisted conventional breeding prior to the actual cloning of the gene. Evaluation of NUE in exotic germplasm is a difficult task due to the wide variation in agronomic factors, such as plant size and architecture, WUE, phenology, and ultimately yield. Component NUE traits identified in exotic germplasm can be backcrossed into existing varieties in order to develop germplasm for mapping the NUE loci and improving the NUE of the existing varieties. New technologies and techniques, such as association mapping and sequencing platforms should benefit this area greatly, and will speed the process of identifying and utilizing beneficial exotic NUE alleles to improve existing varieties. Depending on the trait and the genetic complexity associated with the trait, transgenic approaches may be more suitable than conventional breeding approaches in this case. Crops have been selected under high N, thus potentially limiting NUE under reduced N application. To find variation it may be useful to assess more diverse germplasm. One effort has been made to introgress wild allelic variation into domesticated barley and this indicates the potential of this approach. This effort focused on the introgression of NUE traits from Hordeum sponteneum into adapted barley.89 It may be difficult to introgress traits into commercial varieties if germplasm is too exotic. The genomics field is going through a stage of rapid development and the outcome is that a whole new range of approaches are now not only possible, but viable. Utilizing these advances will make

 REFERENCES

113

improvement of a complex trait, like NUE much more likely than has previously been possible. New approaches moving away from biparental populations to advanced populations, such as NAM and MAGIC populations are now being utilized in efforts to map NUE related traits. Next generation sequencing offers the opportunity for high throughput genotyping of germplasm at low cost and because of this, approaches, such as genomic selection are now being utilized by breeding companies. Major advances in bioinformatics, aided by collaborations through international consortia, such as the International Wheat Genome Sequencing Consortium, will enable progress in genetic improvement of wheat and barley to rival crops, such as maize. As summarized by Moose and Below,15 the integration of quantitative trait mapping, transcriptomics, metabolomics, and transgenics is the next step in breeding for NUE. The work of Zhang et al. is an example of such an integrative approach.93,137 Further transgenic approaches to improve NUE will be identified through hypothesis-based identification of candidate genes, positionally cloned genes in QTL, or screening mutagenized populations. Real progress will be made through improved transformation protocols, gene editing, improved promoter-gene combinations, whole pathway manipulation, stacking multiple genes, and better phenotyping.

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169. Martin A, Lee J, Kichey T, et al. Two cytosolic glutamine synthetase isoforms of maize are specifically involved in the control of grain production. Plant Cell 2006;18(11):3252–74. 170. Cai HM, Zhou Y, Xiao JH, Li XH, Zhang QF, Lian XM. Overexpressed glutamine synthetase gene modifies nitrogen metabolism and abiotic stress responses in rice. Plant Cell Rep 2009;28(3):527–37. 171. Wang Y, Fu B, Pan L, Chen L, Fu X, Li K. Overexpression of Arabidopsis Dof1, GS1 and GS2 enhanced nitrogen assimilation in transgenic tobacco grown under low-nitrogen conditions. Plant Mol Biol Rep 2013;31(4):886–900. 172. Bao A, Zhao Z, Ding G, Shi L, Xu F, Cai H. Accumulated expression level of cytosolic glutamine synthetase 1 Gene (OsGS1; 1 or OsGS1; 2) alter plant development and the carbon-nitrogen metabolic status in rice. PLoS ONE 2014;9(4):e95581. 173. Zhu CG, Chen SL, Zhang GM, et al. The growth improvement of DvGS2-transgenic Arabidopsis thaliana arises from the higher efficiency of nitrogen and carbon assimilation. Plant Biotechnol Rep 2015;9(4):187–95. 174. Zhu CG, Zhang GM, Shen CL, et al. Expression of bacterial glutamine synthetase gene in Arabidopsis thaliana increases the plant biomass and level of nitrogen utilization. Biologia 2015;70(12):1586–96. 175. Urriola J, Rathore KS. Overexpression of a glutamine synthetase gene affects growth and development in sorghum. Transgenic Res 2015;24(3):397–407. 176. Hoshida H, Tanaka Y, Hibino T, Hayashi Y, Tanaka A, Takabe T. Enhanced tolerance to salt stress in transgenic rice that overexpresses chloroplast glutamine synthetase. Plant Mol Biol 2000;43(1):103–11. 177. Migge A, Carrayol E, Hirel B, Becker TW. Leaf-specific overexpression of plastidic glutamine synthetase stimulates the growth of transgenic tobacco seedlings. Planta 2000;210(2):252–60. 178. Chichkova S, Arellano J, Vance CP, Hernandez G. Transgenic tobacco plants that overexpress alfalfa NADHglutamate synthase have higher carbon and nitrogen content. J Exp Bot 2001;52(364):2079–87. 179. Yamaya T, Obara M, Nakajima H, Sasaki S, Hayakawa T, Sato T. Genetic manipulation and quantitative-trait loci mapping for nitrogen recycling in rice. J Exp Bot 2002;53(370):917–25. 180. Good AG, Johnson SJ, De Pauw M, Carroll RT, Savidov N. Engineering nitrogen use efficiency with alanine aminotransferase. Can J Bot 2007;85(3):252–62. 181. Shrawat AK, Carroll RT, DePauw M, Taylor GJ, Good AG. Genetic engineering of improved nitrogen use efficiency in rice by the tissue-specific expression of alanine aminotransferase. Plant Biotechnol J 2008;6(7):722–32. 182. Snyman SJ, Hajari E, Watt MP, Lu Y, Kridl JC. Improved nitrogen use efficiency in transgenic sugarcane: phenotypic assessment in a pot trial under low nitrogen conditions. Plant Cell Rep 2015;34(5):667–9. 183. McAllister CH, Good AG. Alanine aminotransferase variants conferring diverse NUE phenotypes in Arabidopsis thaliana. PLoS ONE 2015;10(4):e121830. 184. Sentoku N, Taniguchi M, Sugiyama T, et al. Analysis of the transgenic tobacco plants expressing Panicum miliaceum aspartate aminotransferase genes. Plant Cell Rep 2000;19(6):598–603. 185. Ameziane R, Bernhard K, Lightfoot D. Expression of the bacterial gdhA gene encoding a NADPH glutamate dehydrogenase in tobacco affects plant growth and development. Plant Soil 2000;221:47–57. 186. Abiko T, Wakayama M, Kawakami A, et al. Changes in nitrogen assimilation, metabolism, and growth in transgenic rice plants expressing a fungal NADP(H)-dependent glutamate dehydrogenase (gdhA). Planta 2010;232(2):299–311. 187. Egami T, Wakayama M, Aoki N, et al. The effects of introduction of a fungal glutamate dehydrogenase gene (gdhA) on the photosynthetic rates, biomass, carbon and nitrogen contents in transgenic potato. Plant Biotechnol 2012;29(1):57–64. 188. Lam HM, Wong P, Chan HK, et al. Overexpression of the ASN1 gene enhances nitrogen status in seeds of Arabidopsis. Plant Physiol 2003;132(2):926–35. 189. Araus V, Vidal EA, Puelma T, et al. Members of BTB gene family of scaffold proteins suppress nitrate uptake and nitrogen use efficiency. Plant Physiol 2016;171(2):1523–32. 190. Yanagisawa S, Akiyama A, Kisaka H, Uchimiya H, Miwa T. Metabolic engineering with Dof1 transcription factor in plants: improved nitrogen assimilation and growth under low-nitrogen conditions. Proc Natl Acad Sci USA 2004;101(20):7833–8.

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191. Kurai T, Wakayama M, Abiko T, Yanagisawa S, Aoki N, Ohsugi R. Introduction of the ZmDof1 gene into rice enhances carbon and nitrogen assimilation under low-nitrogen conditions. Plant Biotechnol J 2011;9(8):826–37. 192. Bi Y-M, Kant S, Clark J, et al. Increased nitrogen-use efficiency in transgenic rice plants over-expressing a nitrogen-responsive early nodulin gene identified from rice expression profiling. Plant Cell Environ 2009;32(12):1749–60. 193. He X, Qu B, Li W, et al. The nitrate-inducible NAC transcription factor TaNAC2-5A controls nitrate response and increases wheat yield. Plant Physiol 2015;169(3):1991–2005. 194. Yu LH, Wu J, Tang H, et al. Overexpression of Arabidopsis NLP7 improves plant growth under both nitrogenlimiting and -sufficient conditions by enhancing nitrogen and carbon assimilation. Sci Rep 2016;6:27795. 195. Xia TM, Xiao D, Liu D, Chai WT, Gong QQ, Wang NN. Heterologous expression of ATG8c from soybean confers tolerance to nitrogen deficiency and increases yield in Arabidopsis. PLoS ONE 2012;7(5):e37217. 196. Paez-Valencia J, Sanchez-Lares J, Marsh E, et al. Enhanced proton translocating pyrophosphatase activity improves nitrogen use efficiency in romaine lettuce. Plant Physiol 2013;161(3):1557–69. 197. Thomsen HC, Eriksson D, Moller IS, Schjoerring JK. Cytosolic glutamine synthetase: a target for improvement of crop nitrogen use efficiency? Trends Plant Sci 2014;19(10):656–63. 198. Halpin C. Gene stacking in transgenic plants—the challenge for 21st century plant biotechnology. Plant ­Biotechnol J 2005;3(2):141–55. 199. Gonzalez N, De Bodt S, Sulpice R, et al. Increased leaf size: different means to an end. Plant Physiol 2010;153(3):1261–79. 200. Palauqui JC, Elmayan T, deBorne FD, Crete P, Charles C, Vaucheret H. Frequencies, timing, and spatial patterns of co-suppression of nitrate reductase and nitrite reductase in transgenic tobacco plants. Plant Physiol 1996;112(4):1447–56. 201. Palauqui JC, Vaucheret H. Transgenes are dispensable for the RNA degradation step of cosuppression. Proc Natl Acad Sci USA 1998;95(16):9675–80. 202. Delwaide AC, Nalley LL, Dixon BL, et al. Revisiting GMOs: are there differences in european consumers’ acceptance and valuation for cisgenically vs transgenically bred rice? PLoS ONE 2015;10(5):e126060. 203. Araki M, Ishii T. Towards social acceptance of plant breeding by genome editing. Trends Plant Sci 2015;20(3):145–9. 204. Waltz E. Cisgenic crop exemption. Nat Biotechnol 2011;29(8):677. 205. Holme IB, Wendt T, Holm PB. Intragenesis and cisgenesis as alternatives to transgenic crop development. Plant Biotechnol J 2013;11(4):395–407. 206. EFSA Panel of GMO. Scientific opinion addressing the safety assessment of plants developed through cisgenesis and intragenesis. EFSA J 2012;10(2):2561. 207. Waltz E. Gene-edited CRISPR mushroom escapes US regulation. Nature 2016;532:293. 208. Furbank RT, Tester M. Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 2011;16(12):635–44. 209. Lynch JP. Roots of the second green revolution. Aust J Bot 2007;55:493–512.

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THE ROLE OF ROOT MORPHOLOGY AND ARCHITECTURE IN PHOSPHORUS ACQUISITION: PHYSIOLOGICAL, GENETIC, AND MOLECULAR BASIS

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Jurandir V. Magalhaes*, Sylvia M. de Sousa*, Claudia T. Guimaraes*, Leon V. Kochian** *Embrapa Maize and Sorghum, Sete Lagoas, Minas Gerais, Brazil **Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada

INTRODUCTION Phosphorus (P) deficiency is a common limitation to plant growth and crop yields on highly weathered soils, which are widespread in agricultural areas of the tropical world, where food security is still an important challenge. On these highly weathered acid soils, P is strongly fixed onto the surfaces of soil clay minerals resulting in low-P availability for uptake by the plants. Adaptive mechanisms that act to enhance internal P utilization efficiency involve transport, partitioning, and remobilization of P within the plant, whereas mechanisms that increase P uptake are associated with alterations in the root system, interactions with microorganisms, and chemical modifications in the rhizosphere.1 Because P is highly immobile in soils with high-P fixation, P acquisition mechanisms are greatly dependent on the proximity of this nutrient to the root system.2 Thus, fine roots leading to increased soil exploitation or more extensive root proliferation in the soil surface (where P is more prevalent) are important adaptive mechanisms to low-P availability. Therefore, changes in root system architecture (RSA) and root morphology can enhance P transport (i.e., diffusion) from the soil to the root surface, thus enhancing P uptake. Indeed, P acquisition efficiency was found to be significantly more important than P internal utilization for maize genotypes cultivated on a P-fixing tropical soil.3 Here we look at the intersection between molecular mechanisms underlying changes in RSA and those associated with plant responses to low-P status, and attempt to identify RSA mechanisms that have possibly evolved to some extent in response to low-P availability in the soil. Such mechanisms are expected to be natural targets for molecular breeding efforts aimed at enhancing P uptake and crop yield on high-P fixing soils, contributing to food security worldwide.

Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00007-7 Copyright © 2017 Elsevier Inc. All rights reserved.

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MOLECULAR BASIS OF RSA AS A MECHANISM ENHANCING P ACQUISITION Roots perform a series of important functions for adequate plant growth and development, such as mediating water and nutrient uptake, promoting anchorage to the soil, and providing an environment where many important biotic interactions take place in the rhizosphere. Understanding the molecular regulation of RSA is therefore of great importance for agriculture. There is a great diversity in root system shapes and architectures, and a number of aspects of RSA can be dynamically controlled, giving rise to root plasticity in response to various environmental stimuli. Furthermore, root structures vary both between and within species due to genetic determinants and environmental factors.4,5 RSA, which is defined as the spatial configuration of the root system over time, is essential for root foraging of essential mineral nutrients and water in different soil zones, and is an important factor in competition between roots of the same or nearby plants.6,7 The main processes that affect RSA are cell division in the primary root meristem (i.e., initial cells), which allows indeterminate growth by the addition of new cells to the root, the formation of lateral roots to increase root exploration capacity, and root hair formation, which increases root surface area of primary and lateral roots.8 Both in monocots and dicots, the first root to emerge is the primary root, which is derived from meristematic tissues that are formed embryonically. The primary and other root types contain meristematic tissue at the root tip, forming the stem cell pool from which different cell types arise.9,10 However, the organization of the root tissue has marked differences between dicots and monocots. In general, root tissues are larger and more complex in monocot species. The primary root of cereals, such as maize (Zea mays L.) and rice (Oryza sativa L.) has from 10 to 15 cortical cell layers and cereal roots have quite large numbers of quiescent cells in the root meristem, usually between 800 and 1220 cells.10,11 Unlike the primary root, lateral roots are postembryonic. In Arabidopsis (Arabidopsis thaliana L.) and most dicots, the pericycle (i.e., tissue located between the vascular central cylinder and the endodermis) is the site of lateral root emergence. Although monocots form primary and lateral roots similarly to dicots, the monocot root system is more complex, forming a “fibrous” root system with various types of branched roots, such as adventitious, brace roots, and seminal roots, which altogether form most of the monocot root system. Adventitious roots have postembryonic origins and are present both in monocots and in dicots. Formed at the root–shoot junction, adventitious roots (i.e., crown or nodal roots) enable plants to better explore more superficial soil layers,10 where nutrients, such as P are prevalent, and also help to anchor and stabilize the plant. RSA differs significantly between monocots and dicots, but the main root adaptive traits that enhance P acquisition are ubiquitously found in all species of vascular plants,12,13 including an increase in root-to-shoot dry weight ratio under P deficiency due to greater stimulation of root growth at the expense of shoot growth.14 The root system in Arabidopsis under low-P conditions is more branched, with reduced primary root growth and enhanced length and number of lateral roots.8,15 In crop plants under P starvation, the elongation of the main root axes is maintained,14 allowing these roots to grow maximally until they encounter localized patches of higher P availability. The response of lateral roots to P stress can exhibit genetic variation within species. For example, depending on the maize genotype, lateral root number, and length may be increased or decreased under P stress.6 Genotypes with increased or sustained lateral root development have enhanced ability to acquire P and maintain growth.16 The genetic control of P acquisition efficiency is complex and so far only a few genes that underlie both root system development and P acquisition have been described. Root morphology and architecture can

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be extremely plastic, and controlled by many genes including transcriptional regulators. Furthermore, sugars, auxin, and ethylene play a role in modulating RSA particularly under P deficiency.10,13,17 PHOSPHORUS-STARVATION TOLERANCE 1 (PSTOL1) is so far one of the few genes that directly links root morphology and P acquisition.18 PSTOL1 is a receptor-like cytoplasmic kinase that is responsible for a major quantitative trait loci (QTL) for P deficiency tolerance in rice.19 PSTOL1 overexpression lines had greater total root length and root surface area18 so as the P uptake and the grain yield in those lines was enhanced by more than 60% under low-P conditions compared to the control. PSTOL1 is expressed in the stem nodes, more specifically in the crown root primordia and parenchymatic cells located outside of the peripheral vascular cylinder, in a region where crown roots are formed in rice.18 Although PSTOL1 cannot directly regulate gene expression, it is possible that it regulates transcription factors through phosphorylation. Microarray data showed that known P-starvation genes were not differentially regulated by PSTOL1 overexpression, but this was not the case for constitutive genes previously implicated with root growth and stress responses.18 One of these genes is a transcription factor, HOX1,20 which is a positive regulator of root cell differentiation, in agreement with a proposed role of PSTOL1 in regulating early crown root development and root growth in rice.18 Auxin has an important role in controlling root development and changes in endogenous levels of this plant hormone under P starvation can shed light onto RSA changes resulting from the crosstalk between auxin and P stress. Several mutants have been identified on the basis of root growth inhibition when low concentrations of auxin were included in the culture media. Some of the key regulators of auxin signaling include: the F-box protein, transport inhibitor response 1 (TIR1) and TIR1-like auxin F-box (AFB) proteins, aux/indole acetic acid (Aux/IAA) proteins, and auxin response factor (ARF) proteins.21 Under low intracellular auxin concentrations, Aux/IAA proteins act as repressors that interact with specific domains of ARF proteins (domains III and IV). The ARF proteins interact with auxin responsive elements in the promoters of downstream genes, regulating their transcription. Under high auxin levels, the interaction between Aux/IAA proteins and the SCF (Skp1-Cul1-F-box)TIR1 E3 Ubiquitin-ligase complex is stabilized, resulting in the degradation of Aux/IAA proteins by the 26S proteasome.22–25 Consequently, the ARF proteins are released from the interaction with Aux/IAA and activate or repress transcription of auxin-responsive genes. One of the mutants that link auxin response to changes in root architecture is rum1, which exhibits impaired growth of seminal embryonic and postembryonic lateral roots.26 RUM1 (ROOTLESS WITH UNDETECTABLE MERISTEM 1) encodes an Aux/IAA family member that interacts with the transcriptional activators, ARF25 and ARF34.27 Thus, rum1 can no longer interact with the SCFTIR1 E3 Ubiquitin-ligase complex, which prevents the functioning of the proteasome-mediated degradation pathway, resulting in the repression of downstream genes.27 The interaction between RUM1 and ZmARF34 was shown by Ludwig et al.28 and these genes were more highly expressed in seminal roots.29 The transcripts TIR1, AFB2, and AFB3 are targets of miR393, which makes the interactions between auxin-based transcriptional regulation and RSA even more complex.30 There is indeed evidence that members of the ARF family are subjected to gene silencing and ARFs possibly involved in root development are targets of microRNAs (miRNAs) or siRNAs. For example, miR160 regulates the transcription factors ARF10 and ARF16 and, consequently, primary and lateral root development.31,32 Auxin transport appears to be critical for root development as gradient differences established by polar auxin transport triggers a series of processes in the plant, including root architecture changes.33,34 The main types of membrane transporters involved in cell to cell movement of auxin are auxin permease1 (AUX1)/LAX influx carriers,35 ATP-binding cassette subfamily B (ABCB) transporters,36 and

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PIN-formed (PIN) efflux carriers.37 In Arabidopsis, ABCB1/PGP1 exports IAA from shoot and root meristematic cells into the long distance polar auxin stream. The ABCB1/PGP1 homologs in maize and sorghum (Sorghum bicolor L.), Brachytic2 (BR2), and Dwarf3 (DW3), respectively, which are expressed in the epidermis and hypodermis of the root apex, are involved in auxin transport from the root apex to the elongation zone.38 The PIN family of auxin transporters has eight members in Arabidopsis (AtPIN1-AtPIN8)39 and several homologs have been cloned and characterized in maize,40 sorghum,41 and rice.42 ZmPIN1a, ZmPIN1b, ZmPIN1c, ZmPIN2, and ZmPIN9, as well as ZmABCB1/BR2 are expressed in the root apex, more specifically in the epidermis, and might play redundant roles in root development.40 The transcription factor, SHORTROOT (SHR) and its target, SCARECROW (SCR), are involved in the specification and localization of stem cells and the quiescent center, controlling radial patterning in Arabidopsis’ roots. Both are transcription factors from the GIBBERELLIC-ACID INSENSITIVEGAI, REPRESSOR OF GAI-RGA AND SCARECROW-SCR (GRAS) family and control not only the initiation of the primary root but also root diameter. Reduced growth of the primary root can result in a shallower root system that may lead to more efficient exploitation of topsoil P. SCR can mediate gibberellic acid and brassinosteroid responses, as well as auxin signaling and is involved in stem cell maintenance.43 A maize homolog of SCR is essential for radicle formation in the coleorhiza.11 Two white lupin (Lupinus albus L.) genes, LaScr1 and LaScr2, that are orthologous to AtSCR, may play a role in cluster root formation and adaptation to P stress.44 Linking P responses to possible root changes mediated by SCR, PHOSPHATE DEFICIENCY RESPONSE2(PDR2) is necessary for maintaining nuclear localization of the SCR protein under low P, which reduces root meristematic activity. The pdr2 mutant45,46 has an exacerbated hypersensitivity to low P, a response that is also observed in siz1, which displays reduced growth of the primary root.47 SUMO E3 LIGASE (SIZ1) is responsible for posttranslational modifications based on the addition of small ubiquitin-like modifier (or SUMO) proteins to the MYB-like transcription factor, PHOSPHATE STARVATION RESPONSE1 (PHR1), and is involved in the control of auxin patterning that modulates RSA under P starvation.48 Moreover, SIZ1 is an element that connects response to low P to reactive oxygen species, inhibiting the growth of the primary root through negative regulation by auxin.48,49 The LOW PHOSPHATE ROOT1 (LPR1) gene and its paralog, LPR2, encode multicopper oxidases and mutations in those genes strongly attenuate the inhibition of the primary root under low P, suggesting the existence of sensing mechanisms related to low concentrations of exogenous P.50 Auxin participates in the modulation of primary root growth under low P, but the precise regulation of this process remains unclear. SIZ1 inhibits lateral root formation at low P and it appears to function independently of the PHR1/SIZ1 signaling pathway.48 Low P and auxin also control root hair length by modulating steady-state levels of ROOT HAIR DEFECTIVE 6-LIKE4 (RSL4) transcript and protein.51 Another gene associated with root morphology in maize is ROOTLESS CONCERNING CROWN AND SEMINAL ROOTS (RTCS), which encodes a transcription factor responsible for the initiation of embryonic seminal roots and all postembryonic shoot-borne roots. RTCS contains a lateral organ boundaries (LOB) domain, LBD, that is induced by auxin52,53 and act downstream of ARF34.54 Lateral organ boundaries domain proteins are involved in several developmental processes and LBD16/asymmetric leaves18 from Arabidopsis is involved in the regulation of lateral root formation, downstream of ARF7 and ARF19.55 RTCS was more highly expressed in a P efficient maize genotype under low-P conditions when compared to a P inefficient genotype,56 suggesting a role in maize performance under low P.

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A series of phosphate transporters (PHT) play pivotal roles in the uptake of inorganic phosphate from the soil. Thirteen PHT1 genes are induced in maize under low-P conditions (the one exception is ZmPHT1;1). The majority of PHT1 genes are mainly expressed in root epidermal and outer cortical cells, in agreement with their direct involvement in P uptake from rhizosphere soil,57 whereas other members are involved in P translocation/remobilization.58 PHT1;5, for example, plays a critical role in mobilizing P from source to sink organs in accordance with developmental cues and P status. Furthermore, when overexpressed in Arabidopsis, PHT1;5 led to an increased root hair formation and reduced primary root growth.58 Several PHT1 family members are overexpressed in the presence of arbuscular mycorrhizal fungi that form the symbiotic association with roots that is important for P acquisition. In rice, 70% of the total acquired P is delivered via this root/mycorrhizal route.59 There is evidence that the interaction between arbuscular mycorrhizal fungi and rice roots not only increases plant phosphate uptake, but at the same time modulates RSA, promoting an increase in lateral root density.59a Loss of function of the PHOSPHATE 2 gene, PHO2, results in phenotypes consistent with perturbed phloem transport of P between Arabidopsis shoots and roots,60 and its rice homolog, LEAF TIP NECROSIS1 (LTN1), has been associated with root morphology changes under low P. The LTN1 mutant exhibits increased P uptake and translocation, which results in P over accumulation in shoots. In association with enhanced P uptake and transport, some P transporters were upregulated in the LTN1 mutant in the presence of sufficient P. Furthermore, the elongation of primary and adventitious roots was enhanced in the LTN1 mutant under P starvation, suggesting that LTN1 is involved in P-dependent root architecture alterations. It was shown that LTN1 is a crucial P starvation signaling component downstream of miR399, which is involved in the regulation of multiple P starvation responses in rice.61 The possible role of miRNAs, such as miR399 and others in P-triggered RSA changes is discussed in a later section of this chapter. There are a number of other transcription factors involved in root architectural changes linked to changes in P status. ZAT6, a Cys-2/His-2 Zinc finger transcription factor, positively regulates lateral root growth and negatively regulates primary root growth, independently of P status.62 The MYB transcription factor, AtPHR1, upregulates a series of Pi-starvation inducible genes.63 The rice homolog of AtPHR1, OsPHR2, is a key regulator of P starvation signaling. OsMyb2P-1, when overexpressed in rice, led to more lateral roots and a longer primary root compared to the wildtype under low-P conditions.64 WRKY transcriptional factors comprise a large gene family of regulatory proteins in plants. Recently, Dai et al.65 showed that WRKY74, when overexpressed in rice, led to a larger root system phenotype, enhanced P acquisition and grain yield. Moreover, there are also evidences indicating that OsWRKY74 positively regulates miR399, which plays a pivotal role in P homeostasis. There is an increase in ethylene biosynthetic genes under low-P stress, but this response is dependent on root types and the phase of root growth.66 The ethylene overproducer 1(eto1) and constitutive triple response 1(ctr1) mutants have reduced formation of lateral roots under low P. On the other hand, ethylene insensitive 1(etr1) has increased laterals root and stronger inhibition of primary root growth under low P,67 suggesting that ethylene regulates P responsive primary root elongation and some aspects of lateral root growth. There is evidence that ethylene stimulates auxin biosynthesis and that the synergistic interaction between these hormones modulates root growth and root hair development.68,69 However, the precise role of ethylene in P responses remains unclear. Pathways involving the plant hormone, cytokinin, are activated as a secondary response to cross talk between auxin and P signaling. Under normal conditions, cytokinin inhibits root growth, but under low-P conditions, cytokinins are repressed, alleviating the inhibition of root growth.70 The cytokinin

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receptor, CYTOKININ RESPONSE 1 (CRE1) has lower expression under low-P availability.71 On the other hand, the cytokinin oxidase/dehydrogenase gene, CKX, which encodes a protein that catalyzes the degradation of cytokinins, is upregulated in white lupin cluster roots in response to P deficiency.70 Cluster or proteoid roots are a P acquisition strategy of some plant species in the Proteaceae family and are primary lateral roots that develop one or more clusters of rootlets along their axes. Proteoid roots synthesize large amounts of citrate, which is subsequently released into the rhizosphere of Pstarved plants to increase P availability in the rhizosphere.72–74 In these species P deficiency triggers metabolic changes necessary for the large increase in citrate accumulation, including activation of several enzymes involved in organic acid synthesis and catabolism by low P.75 Sugars are required for P starvation responses including cluster root formation and the expression of P starvation-induced genes. Two genes that regulate cluster root formation in white lupin, the high-affinity phosphate transporter, LaPT1, and phosphoenolpyruvate carboxylase 3 (LaPEPC3), are upregulated by both P starvation and sucrose. Acid phosphatase secretion mediated by LaSAP is however stimulated by sucrose independently of P status, suggesting there are at least two distinct sugar-signaling pathways related to P responses in white lupin roots.76 Sugars can act as signaling molecules that can modulate RSA via transcriptional upregulation of P responsive genes under low-P availability. For example, in Arabidopsis there is an increase in lateral root formation associated with sugars and P deficiency.77 Increases in root cell size and changes in shape can also involve alterations in turgor pressure via uptake of sugars as a cell osmoticum. This process can be modulated by the activity of invertases which cleave dissacharides to monosaccharides prior to cellular uptake via signaling pathways between the cytoplasm and the exterior of the root cell.78 One class of proteins that possibly mediate this cross talk are wall-associated kinases (WAKs).79 Mutants that lack specific WAK proteins can have alterations in function of other cell wall proteins, suggesting that WAKs mediate various intermediate steps in the cell elongation process, including those controlled by expansins, which are cell wall proteins involved in the loosening of cell walls during cell expansion.79 The Arabidopsis wak2 mutant requires the addition of higher levels of sugars and salts to the growth media, as the mutant has reduced vacuolar invertase activity in roots that reduces the capacity for osmoticum-induced increases in turgor.80 The extracellular domain of certain WAKs binds to cell wall pectins and links cell wall modification during cellular expansion to solute metabolism via a signal transduction pathway involving MITOGEN-ACTIVATED PROTEIN KINASES and invertase induction.80,81 WAKs have been implicated in several plant responses to biotic and abiotic factors, possibly including P stress.82

THE ROLE OF miRNAs IN RSA AND P ACQUISITION miRNAs have recently emerged as fundamental players controlling RSA dynamics and P homeostasis in plants, particularly as sensing molecules with a potential role in environmentally triggered adaptive responses to abiotic stresses, such as P deficiency. On low-pH tropical soils, P diffusion from the soil to the root surface is strongly limited by P fixation into the soil clays, making P deficiency a serious constraint for agricultural production worldwide. Particularly on highly weathered tropical soils, P diffusion is highly dependent on the soil water content,83 which varies during the crop season. In fact, P diffusion is essentially interrupted when soil moisture is reduced (but remains high enough not to cause drought stress). Thus, as suggested by Novais et al.,83 P supply to the plant and consequently P uptake, especially in high P-fixing tropical soils, are apparently highly discontinuous and closely dependent

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on the soil water content. Strongly constrained P diffusive fluxes in high P-fixing soils also give rise to extensive spatial variation in soil P concentrations. Therefore, adaptive mechanisms underlying P deficiency tolerance in high P-fixing soils, which ultimately contribute to yield stability in low-P environments, can also be expected to be dynamically regulated. Possibly responding to external P via signaling pathways, these mechanisms need to quickly come into play in response to P deficiency to increase the efficiency at which plants acquire or utilize internal P. Fine tuning should act as a safeguard to avoid unnecessary energy costs when the energy crisis caused by the lack of P is alleviated or reduced. The state-of-the-art regarding our understanding of miRNAs in the realm of P responses and root development clearly make these molecules strong candidates for a pivotal role in dynamic mechanisms that enhance P efficiency. However, the actual role of miRNAs in agronomic performance in low-P soils has yet to be shown, precluding the development of miRNA-based strategies to enhance yield stability under low-P availability. A possible way to manipulate P homeostasis is target mimicry, whereby miRNA399, a key player in P homeostasis, is trapped by the long noncoding RNA, INDUCED BY PHOSPHATE STARVATION1 (IPS1) in Arabidopsis, suppressing miR399-mediated PHO2 transcript degradation.84 Accordingly, sequence complementary exists between miR399 and IPS1 but a mismatch at the cleavage site interrupts pairing, preventing IPS1 cleavage. Thus, overexpression of IPS1 leads to increased accumulation of PHO2 and reduced shoot P content due to miR399 sequestration. Recent comprehensive reviews are available on P deficiency tolerance in plants,85 the role of miRNAs in root cell specification and RSA,86 and P responses and signaling.87–89 Readers are directed to those reviews for a more thorough background in this area of research that may ultimately play a key role in enhanced crop performance in low-P environments. Our explicit goal here is to find logical intersections between these three, sometimes unconnected areas of research. We will discuss the potential role of miRNAs specifically in modifying RSA in response to low-P conditions, which is expected to increase P diffusion from the soil clays toward the root surface leading to enhanced P uptake. Under low-P availability, these mechanisms are expected to contribute perhaps dramatically to yield stability in large agricultural areas of the world. miRNAs are 21–25 nucleotide noncoding RNA molecules that repress gene expression posttranscriptionally, via binding to target mRNAs driving them to degradation, and may also act as translational repressors.90 Accordingly, there is a relationship between the degree of miRNA/target complementarity and the mode of action of miRNAs. In plants, nearly perfect complementarity more often leads to repression of gene expression by mRNA cleavage.91,92 Particularly with regards to P responses in plants, translational repression also occurs although plant miRNAs are more often involved in posttranscriptional downregulation of gene expression.93,94 Additionally, in Arabidopsis under stress conditions, retrotransposon-derived 21–22 epigenetically activated small interfering or easiRNAs are produced and this class of small RNAs could possibly lead to transcriptional gene silencing via DNA methylation.95

DOES miR399 PLAYS A ROLE IN ENHANCING P UPTAKE VIA MODULATION OF RSA? Work in Arabidopsis showed that miR399 is highly and specifically induced by P stress and targets the 5′ untranslated region of an ubiquitin conjugating enzyme (UBC) for degradation.96 The fact that this miRNA is induced specifically by P stress (no miR399 upregulation was observed with low-K or -N treatments) suggests that miR399 evolution has been shaped by selection footprints related to P availability. In addition, miR399 is conserved and shows similar P responses across a broad evolutionary continuum as it is present and functions similarly in other species including maize,97 beans,98 and rice,93

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suggesting functional conservation with regard to P responses. This leads to the consideration that miR399 is a possible hub for P efficiency mechanisms with potential utility in crop breeding. However, if it indeed is a central player and exactly how miR399 can be used for this purpose are complex questions that will require findings from specific research strategies to be properly answered. Arabidopsis lines overexpressing miR399 showed enhanced P uptake and excessive P accumulation in shoots leading to phytotoxicity when the P supply was high.99 These authors proposed a threepronged role of miR399 in P homeostasis based on P acquisition, allocation, and remobilization; under P deficiency miR399 enhances not only P uptake but also root-to-shoot P transport. Aung100 showed that the Arabidopsis P overaccumulating mutant, pho2, is caused by a premature stop codon in the UBC24 gene that encodes the ubiquitin-conjugating E2 enzyme that is the primary target of miR399. Both UBC24 and miR399 are localized in vascular tissues and, as expected, miR399 overexpression lines phenocopy pho2. Because miR399 was found to be transported through the phloem from shoots to regulate P acquisition in roots,101 this miRNA represents a systemic signal for plant P homeostasis.102 In the pho2 mutant, P starvation-responsive genes remain induced under normal P conditions, suggesting that pho2 represses P-starvation responses.93 Under P sufficiency in the pho2 mutant, miR399 function leads to excessive P accumulation in the shoot resulting in P toxicity. However, miR399 can also enhance P acquisition from the external media via acid phosphatase and H+ release, in addition to enhanced expression of P transporters,103 which could play important roles in adaptation to low-P conditions. Nevertheless, particularly for high P-fixing soils, external P is tightly and rather permanently fixed to the soil clays. Therefore, mechanisms that act to enhance P diffusion, such alterations in root morphology, could have a more important impact in overall crop adaptation to low-P conditions in comparison to enhancement of P transporter activity. There are a number of indications that miR399 can trigger root modifications to fulfill such a role,104 though by as yet elusive mechanisms. The PHO2 pathway can impact root morphology, which is suggested by mutations in LTN1, the putative rice ortholog of Arabidopsis PHO2.61 The ltn1 mutant showed increased P uptake and translocation leading to P over accumulation in shoots and a leaf necrosis phenotype, similar to pho2. In turn, ltn1 exhibited enhanced elongation of the primary and adventitious roots under P starvation, suggesting that miR399 downregulation of PHO2 may be involved with changes in root architecture. Another way miR399-triggered RSA changes can happen is via OsPHR2, a MYB gene homolog of Arabidopsis PHR1.102 PHR1 is involved in P responses and AtPHR1 is a positive regulator of miR399.93,105 Rice plants overexpressing OsPHR2 showed increased P accumulation in roots and longer and more numerous root hairs.102 In rice OsPHR2 overexpressing plants, miR399 and PSI genes are upregulated,102 suggesting a role of miR399 in root hair formation, although other genes associated with root phenotypes may be targeted by AtPHR1. A broader role for miR399 in RSA via MYB-mediated transcriptional regulation is however possible. AtMYB2 has been shown to directly bind to the miR399f promoter to regulate its expression.106 These authors showed that overexpression of AtMYB2 affects RSA via suppression of primary root growth and enhanced root hair development. As AtMYB2 and miR399 were expressed in the same vascular tissues and were both induced under P starvation, this suggests that AtMYB2-mediated miR399f expression may be involved in RSA changes via P-starvation responses. Another instance of transcription factors/miRNAs leading to RSA changes is based on miR396a, which was shown to repress the Basic Helix-Loop-Helix transcription factor, bHLH74, reducing root growth in Arabidopsis.107 This was consistent with longer roots observed in the T-DNA miR396a-1

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131

mutant, shorter roots arising from overexpression mi396a-1, and longer roots also seen in a line overexpressing the miR396-resistant version of bHLH74. Bao et al.107 found that the reduced root growth phenotype occurred via decreased length of differentiated cortical cells in the root elongation zone. In their seminal work, Fujii et al.96 also observed effects of miR399 on root growth. Accordingly, transgenic Arabidopsis plants expressing the target of miR399, the UBC mRNA (later shown to be encoded by PHO2) without the 5’ untranslated region (hence, deregulated for miR399), showed less inhibition of primary root growth and reduced expression of the phosphate transporter, AtPT1, under low P. These authors point out that the attenuation of primary root elongation, increased lateral root and root hairs and the induction of P transporters can all contribute to P acquisition. However, in this study little effect of miR399 regulation of UBC on lateral root growth and root hair proliferation under low P were reported. The body of evidence on root architecture dynamics in relation to miR399 suggest this miRNA can be manipulated to enhance P acquisition via RSA dynamics, although further work is needed to demonstrate the relevance of that in plant breeding. However, given its multiple roles in P homeostasis, miR399 does deserve further investigation, particularly for crops grown specifically under P-deficient conditions, as the miR399-elicited defect in P remobilization under low-P conditions was not reported to be as severe as in P sufficient conditions in Arabidopsis.108

OTHER miRNAs POTENTIALLY INVOLVED IN RSA CHANGES IN RESPONSE TO P Under P starvation in Arabidopsis, the miRNAs miR156, miR399, miR778, miR827, and miR2111 were upregulated whereas miR169, miR395, and miR398 were downregulated.109 A recent study110 implicated miR156 in lateral root development as plants overexpressing miR156 showed enhanced lateral root formation whereas reduced miR156 expression was associated with fewer roots. The miR156 targets, SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) genes, SPL3, SPL9, and, particularly, SPL10, have been implicated in the repression of lateral root development that is counteracted by miR156. Yu et al.110 suggested that the miR156/SPL module affects the rate of lateral root primordia progression rather than lateral root initiation. The response of both miR156 and SPL9 and SPL10 to auxin during lateral root development is very interesting. Given that auxin has a known role in lateral root formation,21 the work by Yu et al.110 in conjunction with the upregulation of miR156 by P starvation, suggests that miR156/SPL may integrate root responses related to P starvation and auxin signaling pathways. In fact, Yu et al.110 indicated that auxin induced both miR156 and SPL genes, which antagonize lateral root development. Based on that response, the authors proposed that auxin induction of SPL genes may counterbalance enhanced lateral root formation due to miR156-based repression of SPL genes. Differently than miR156, miR169 expression is downregulated by P starvation.109,111 Using target mimicry, Sorin et al.112 showed that the miR169defg isoform and its targets, which are transcription factor genes encoding subunit A of nuclear factor Y (NF-YA), NF-YA2, and NF-YA10, control the root apical meristem and lateral root density in Arabidopsis. In this work, NF-YA2 regulation by miR169defg was also implicated in lateral root initiation, with deregulation of NF-YA2 increasing lateral root initiation. Strengthening the link between miR169/NF-YA2 and NF-YA10 and root modifications in response to P starvation, Woo et al 113 found that these two genes were upregulated in response to P starvation in Arabidopsis’ roots. In Arabidopsis, overexpression of miR778, which is upregulated by P starvation,109 was associated with increased root elongation, which was reduced in miR778 target mimic plants.114 miR778

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overexpression possibly has potentially multiple effects, influencing phosphate transport, root development, and Pi homeostasis and suggesting a potential role of this miRNA in fairly broad adaptation to low-P conditions. As indicated by Wang et al.,114 this could happen via its target, Su(var) 3–9 homologs 6 (SUVH6) that encodes a histone H3 lysine 9 (H3K9) methyltransferase, which could lead to gene silencing via DNA methylation.115 There are indeed precedents for epigenetic mechanisms related to DNA methylation playing a role in P stress responses including regulation of P-starvation responsive genes.116 Finally, a recent study by Nie et al.117 indicated that miR393, miR408, and miR444, which have been previously implicated in root development phenotypes during abiotic stresses, were differentially expressed in maize in early stages on P starvation. This suggests a link between these miRNAs and root changes under P stress, which could possibly also integrate the miR393/auxin receptor, AFB3 N-responsive module118 also with P responses. This is conceivable in view of similar root responses triggered by auxin and low-P conditions, though it is possible that RSA changes to low P are both auxin dependent and independent.87

QTL FOR ROOT TRAITS UNDER P DEFICIENCY CONSISTENTLY AFFECTING YIELD PERFORMANCE IN THE FIELD Several QTLs for root related traits were mapped in maize cultivated under P sufficient and P deficient conditions in nutrient solution,119–121 in a soil-based greenhouse trial122 and in the field.123–127 Based on those studies, a metaanalysis strategy integrated 191 QTLs associated with root and shoot phenotypes and yield components to assess low-P tolerance in maize.128 A total of 23 consensus QTLs (cQTLs) were identified on 8 of the 10 maize chromosomes, representing 131 original QTLs, where many candidate genes previously associated with P-efficiency mechanisms are located.128 Also, Azevedo et al.129 used a multiple trait-multiple interval mapping strategy to identify 13 genomic regions associated with root morphology, biomass accumulation and P content in maize seedlings under P deficiency. Five of those regions coincided with cQTLs integrating different traits related to low-P tolerance.128 Additionally, many other studies have reported QTL associated with different root traits under a range of conditions, mainly in hydroponics.130–136 Our goal in this section is to integrate the current studies reporting on QTLs associated with root morphology and low-P tolerance (Table 7.1). As most of the genetic maps for these studies were based on RFLP and SSR markers, their physical positions were inferred based on the B73 RefGen_v2 sequence, available at MaizeGDB (www.maizegdb.org). Some of the root morphology QTLs detected under P deficiency were also reported without P deficiency stress and in different genetic backgrounds. This suggests that at least part of the genetic basis of low-P tolerance via changes in root morphology is rather constitutive, overlapping with root development. In maize, cQTL1-1 for low-P tolerance128 colocated with QTLs for root traits in hydroponics,130–133 which were detected in a meta-QTL study (Rt-1).134 The QTL, qSRN-1.2, which was associated with seminal root number assessed in paper rolls and pots using a B73 × Gaspé Flint maize introgression library,136 mapped to the same region as cQTL1-1 (Table 7.1). Interestingly, this genomic region harbors RTCS, a gene involved with root initiation53 as previously discussed. Salvi et al.136 noted that a number of root morphology QTLs mapped in the vicinity of qSRN-1.2, which appears to also harbor QTLs for low-P tolerance in maize.

 QTL FOR ROOT TRAITS UNDER P DEFICIENCY

133

Table 7.1  Integration of Maize QTLs Associated With Root Morphology and Agronomic Traits Assessed in Hydroponics, Pots, and in the Field Under Stressed and NonStressed Conditions QTLs

Bin

Flanking Markers

cQTL1-1

1.01-02

R1L, R1W, DTI

1.02

umc157

bnlg176

PrL

1.01

PGAMCCC210

umc157

MaxAxRL

1.02

phi427913

bnlg1614

NoAx

1.01

umc1041

bnlg1178

Rt-1

1.01

qSRN-1.2

1.01-03

RTCS

umc1685

Traits

References

11.61

14.13

Low-P tolerance traits

Zhang et al.128

12.04

33.16

Length and weight of primary roots, drought tolerance index

Tuberosa et al.130

12.04

Length of primary roots

Hund et al.131

9.26

13.51

Maximal length Liu et al.132 of axial roots

6.23

14.95

Number of axillary roots

Trachsel et al.133

Metaanalysis for root traits

Hund et al.134

10.38

46.68

Number of seminal roots

Salvi et al.136

1.01

10.83

11.70

RTCS

Taramino et al.53

cQTL1-2

1.03

33.16

35.74

Low-P tolerance traits

Zhang et al.128

R2W

1.03-04

csu145c

asg45

34.78

51.96

Weight of adventitious roots

Tuberosa et al.130

PrW, PrAx_D, SeAx_C, L

1.03-04

umc11a

asg45

34.65

51.96

Weight of primary roots, count, and length of seminal axile roots

Hund et al.131

SolPriLen

1.03

41.50

46.35

Length of second-order lateral, primary roots

Burton et al.135

RPF

1.03-04

csu145c

asg45

34.78

51.96

Root pulling force

Landi et al.137

AxRL

1.03-04

umc1403

bnlg1484

15.80

46.10

Length of axial root

Cai et al.127

Ax-1, Rt-2

1.03-04

Metaanalysis for root traits

Hund et al.134

qMulti1.03

1.03

RD, R:S, TDW

Azevedo et al.129

bnlg1083

phi001

Physical Position

PZA03742_1

27.50

44.50

(Continued)

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Table 7.1  Integration of Maize QTLs Associated With Root Morphology and Agronomic Traits Assessed in Hydroponics, Pots, and in the Field Under Stressed and NonStressed Conditions (cont.) QTLs

Bin

Flanking Markers

Physical Position

Traits

References

PH, GY,KN,100KW

1.03

umc1403

15.80

51.51

Plant height, grain yield, kernel number and weight of 100 kernels in low P

Cai et al.138

cQTL1-3

1.07

205.79

210.36

Low-P tolerance traits

Zhang et al.128

Rt-3

1.06

Metaanalysis— root traits

Hund et al.134

R2W, R1D, DTI, GY

1.06-07

php20644

umc128

197.40

226.89

R2W, R1D, DTI, GY

Tuberosa et al.130

qMulti1.07

1.07

PZA01963_15

PHM12693_8

203.70

223.50

RL, SA, SA2, R:S, TDW, Pcont

Azevedo et al.129

PH

1.06-07

umc1335

bnlg1556

152.00

207.00

Plant height, grain yield, kernel number, and weight of 100 kernels in low P

Cai et al.138

RPF

1.06

PGAMCGG220

umc128

226.89

Root pulling force

Landi et al.137

qPUTIL1

1.07

PHM5480_17

PHM12693_8

204.85

224.17

P utilization efficiency

Mendes et al.139

qMulti8.02

8.02

ZmPSTOL8.02

PHM1978_111

13.3

21.8

RL, SA, SA2, R:S, Pcont

Azevedo et al.129

LPrAx

8.02-03

umc103a

bnlg669

16.7

25.3

Length of primary roots

Trachsel et al.133

qSDW

8.02

phi119

umc1034

12.5

20.7

Shoot dry weight

Cai et al.127

qRL8.05

8.05

PHM934_19

ZmPSTOL8.05_1

116.8

152.0

Total length of roots

Azevedo et al.129

R1L

8.05

bnlg162

Length of primary roots

Tuberosa et al.130

SemNum

8.05

qSRN-8.5

8.05

bnlg1863

qPAE8

8.05-06

PZA00951_1

bnlg1866

133.0 122.75

123.1

Number of seminal roots

Burton et al.135

umc1846

92.1

130.7

Number of seminal roots

Salvi et al.136

PZA00706_16

155.5

162.5

P acquisition efficiency

Mendes et al.139

QTL FOR ROOT TRAITS UNDER P DEFICIENCY

135

Improved root traits, particularly those assessed in seedlings, do not necessarily translate into enhanced plant growth and yield in mature plants. Therefore in this section we describe instances of root development QTL that were coincident with genomic regions linked to agronomic performance in the field, for maize cultivated in different environments. QTLs spanning bin 1.03 that were associated with tolerance to low-P conditions in maize (cQTL1-2)128 also appear to control different root traits in nutrient solution130,131,135 and in soil.127,137 Two meta-QTLs designated as Ax-1 and Rt-2 were found in this region by Hund et al.,134 and this region also harbors QTLs for multiple root traits in nutrient solution under low-P conditions129 and for grain yield, plant height, kernel number, and weight in a low-P soil.138 QTLs associated with root traits at the seedling stage in hydroponics and with grain yield under different water regimes were mapped to bin 1.06–1.07.130 Within this region, root yield 1.06 was subsequently validated as a major QTL constitutively controlling root and agronomic traits in maize.140 Also in this region are QTLs for root-pulling resistance in adult plants,137 QTLs for P utilization efficiency based on grain yield on a low-P soil139 and QTLs for root traits and total seedling dry weight in hydroponics.129 This region of the maize genome also coincided with a cluster of QTLs in two metaanalysis studies, one combining different root traits (Rt-3)131 and another including P-deficiency tolerance traits (cQTL1-3)128. The association between QTLs underlying root traits in seedlings with yield performance suggests that some of these QTLs might be used in marker-assisted selection programs to improve yield stability under drought and other mineral nutrient stresses in maize. Although a number of QTLs controlling root morphology under P deficiency have been identified, only a few genes underlying root morphology with a joint effect on P efficiency have been described. A notable example is PSTOL1, which encodes a serine/threonine kinase that was shown to enhance early root growth and P acquisition in rice.18 A major QTL controlling phosphorus uptake (Pup1) was mapped to rice chromosome 12, explaining approximately 80% of the phenotypic variance.19 Rice near isogenic lines carrying Pup1 showed a three-fold increase in P uptake and enhanced roo-t surface area when grown in a P-deficient soil.19,141 Additionally, when compared to their parents, irrigated, and upland rice varieties introgressed with Pup1 showed significant grain yield increases on different low-P soils.142,143 OsPSTOL1 was characterized as the gene underlying the Pup1 locus, which enhanced grain yield under low-P conditions and improved P uptake in two transgenic rice varieties,18 presumably due to a larger root system (i.e., root length and total root surface area) elicited by OsPSTOL1. A joint linkage-association mapping approach revealed that, in sorghum, multiple homologs of OsPSTOL1 affecting root morphology and architecture in hydroponics also enhanced grain yield in sorghum cultivated in a low-P soil.82 These genes, collectively designated as Sorghum bicolor PSTOL1 (SbPSTOL1), increased biomass accumulation and P content in sorghum landraces from West Africa under low P, suggesting a stable effect of the target alleles across environments and genetic backgrounds.82 In maize, four genes sharing high sequence identity and a conserved serine/threonine kinase domain with OsPSTOL1 were preferentially expressed in roots and colocalized with QTLs associated with root morphology and P acquisition traits.129 One of these genes, ZmPSTOL8.05_1, was mapped at bin 8.05 on chromosome 8, flanking a QTL for root length under P deficiency,129 which coincided with QTLs previously detected for root length130 and for seminal root number using the maize intermated B73 × Mo17 (IBM) population135 and maize introgression lines (qSRN-8.5; Salvi et al.).136 Furthermore, Mendes et al.139 mapped QTL to this same region for P acquisition efficiency based on grain yield in a low-P soil. Another maize OsPSTOL1 homolog, ZmPSTOL8.02, colocalized with qMulti8.02, a QTL controlling multiple traits evaluated under low-P conditions in maize seedlings,129 which coincided with QTLs for primary root length in nutrient solution133 and with shoot dry weight in the field.127

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Taken together, these studies suggested that the PSTOL1 gene family may share a conserved function in altering root morphology across rice, sorghum, and maize, which strengthens the potential for utilizing those genes for improving P acquisition in crop plants. Although the molecular mechanisms by which PSTOL1 proteins act to increase P uptake via root morphology changes are still unclear, molecular breeding strategies targeting positive alleles of PSTOL1 show promise as a tool to improving yield stability under low-P soils, which are prevalent in large tropical and subtropical areas in the world.

NOVEL ROOT SYSTEM IMAGING METHODS AND THEIR USE TO INVESTIGATE THE ROLE OF RSA IN IMPROVING P ACQUISITION EFFICIENCY It is widely accepted that RSA plays important roles in acquisition of limiting nutrients and water from the soil. This is particularly true for the most immobile and mobile nutrients in the soil, P and water. Particularly on acidic tropical soils, a large proportion of the P is fixed in the soil because phosphate anions bind assiduously to iron (Fe) and aluminum (Al) oxides on the surface of clay minerals, and also are incorporated by soil microbes into organic P. Because of the often dramatically heterogeneous distribution of soil P, a strategy that both wild and cultivated plant species have adopted involves, as described by Lynch et al., topsoil P foraging. This involves in P efficient genotypes the proliferation of relatively ageotropic lateral roots into the topsoil that are longer and thinner with more dense and longer root hairs, to reduce the diffusive distance between soil P and the root.144–146 On the other hand, because water is the most mobile nutrient in the soil, deep-rooted crop lines are superior at water acquisition under drought. Recently, the first deep rooting gene, DRO1, was identified via positional cloning in rice, and marker-assisted introgression of this gene into shallow rooted rice genotypes significantly increased root system depth and improved grain yield under drought in the field.147 Thus it is clear that plants have adopted strategies to place a significant portion of their root system in the soil regions where the nutrients are located, in order for the roots to take advantage of their repertoire of chemical and biotic adaptations to increase the availability and uptake of those nutrients. The growing realization of the importance of RSA to nutrient acquisition under limiting conditions, as well as recent technological advancements in image capture and computer-based image analysis has resulted in an explosion in high throughput root phenotyping and analysis platforms. These imaging techniques are beginning to allow researchers to analyze and genetically map RSA traits to identify genes and markers responsible for agronomically important RSA traits. This in turn should provide fundamental information for facilitating effective molecular breeding approaches for improved crop nutrient efficiency. In a perfect world, researchers would be able to carry out high throughput RSA phenotyping on plants in the field, which would reduce the possibilities of artifactual data due to growth in much simpler media. However, it is obvious that soil is a serious impediment to observing complex root structure in detail. Furthermore, current methods for imaging roots in both the lab and in the field have a relatively low throughput, which is a serious problem for genetic analyses requiring phenotyping of a significant number of genotypes. Therefore, root imaging techniques based on root growth in simpler and more transparent systems, such as gels and hydroponics have often been employed to deal with the throughput issue while still generating meaningful data. To that end, a large number of different

 NOVEL ROOT SYSTEM IMAGING METHODS

137

systems have been developed and employed to quantitatively evaluate root system architectural features that underlie important agronomic traits.148–150 Much of modern root measurement for phenotyping purposes has been driven by the development of laboratory and greenhouse-based growth methodologies along with the simultaneous expansion of imaging and analysis techniques and germplasm resources. To date, laboratory growth methods include hydroponics, agar plates, paper/cloth pouches, gel plates, box and cylinder growth systems, and aeroponic arrangements, while greenhouse growth methods typically include pots, cylinders, plates and troughs that are filled with soil, soil substitute, or sand mixtures. Rhizotron and minirhizotron growth methods have also been developed to complement coring, trenching and shovelomics techniques in field and greenhouse settings. Additionally, root system image capture techniques have been expanded. Digital flatbed scanner and camera systems are the most ubiquitous today, however methods using X-ray radiography, neutron radiography, laser scanning, magnetic resonance imaging, positron emission tomography, computerized tomography, and microcomputerized tomography have also been demonstrated and refined to image crop root system in both two and three dimensions. Because of space limitations, in this section we will focus on our research group’s root 3D-imaging platform, RootReader 3D151 and improvements we have been making in this system to develop a high throughput, low cost root phenotyping system.152 The RootReader 3D platform collects a series of 2D digital images of a root system as the plant (and root system) are rotated through 360 degree. The silhouette of the root system in each view is used to identify pixels where the background is visible, meaning no portion of the root is obscuring the imaging path to that pixel. All of the voxels (3D equivalent of pixels) that occur along that imaging path are designated as empty. This is repeated for every pixel in each image, which generates a collection of voxels representing the absence of root tissue. Hence, the remaining voxels not designated as empty make up the 3D RSA reconstruction. Another way to look at this type of RSA image reconstruction is that the RSA image is what remains after those regions of the image volume have been removed where it has been possible to exclude the presence of roots. Our first RootReader 3D system described in Clark et al.151 was based on the roots grown in glass cylinders housing gellan gum plus mineral nutrients. The roots were grown under close to sterile conditions. Imaging of the root systems involved immersing the gel-filled cyinder in a square glass chamber filled with water (minimizing refraction), and then imaged as the plant was rotated through 360 degrees. Subsequently the RootReader 3D software processed the images and reconstructed the series of 2D images into a 3D root model. Then the RootReader 3D software would classify the roots into different types, and quantify a series of root traits that described both static and dynamic root architecture, topology, and morphology traits. This system has proven valuable, for example, in conducting a genome-wide association study on rice RSA.153 Although this approach has proven to be valuable in genetic analyses of crop RSA, it has several serious disadvantages. Although the system has relatively good throughput allowing the imaging of up to 100 root systems a day, there are significant labor costs for sample preparation, as the preparation of the gel and nutrient solutions, autoclaving, sterile filtering, pouring of gels, and planting under sterile conditions are all labor intensive. Also, a nontrivial number of plants are lost because of fungal and bacterial contamination on the roots in gel. There is another more serious biologically-based limitation to the gel based system. Although the rice roots grew well in the gel cylinders, when we used it for other crop species including maize, sorghum, and soybean, their roots did not grow optimally in the gellan gum media. Their root systems were smaller and somewhat stunted compared to their root systems grown in hydroponics.

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These limitations forced us to develop a new root growth system that would also preserve the root system’s 3D architecture. The solution was to grow the roots hydroponically in a tower of sequential plastic mesh disks supported by plastic threaded rods. The plastic mesh discs are made from acrylonitrile butadiene styrene plastic using a 3D printer, and the intervals between the discs increase in distance as one moves down in the z direction (away from the plant seed). This system allows the roots to grow relatively freely while the plastic mesh system helps to maintain 3D RSA (Fig.7.1). One issue that arises with this new system is that the plastic mesh grids obscure a thin slice of the root system image. In the initial version of the RootReader 3D sofware employed for analyzing root images from the plastic mesh tower/hydroponic growth system, a separate algorithm was added to the software to computationally remove the mesh image before thresholding and skeletonization of the root images. This resulted in thin gaps in the 3D reconstructions of the RSA. More recently, the thresholding software has been improved by incorporating a new thresholding algorithm which removes everything from the image that is not root (background and plastic mesh; Dave Schneider, personal communication) which produces significantly improved 3D reconstructions with little or no gaps in the reconstructed root systems. For details on the new version hydroponic-based RootReader 3D system, see Ref. [152]. This new hydroponics-based system provides a number of advantages over the earlier gel-based root growth system. First, we now can study any plant species, especially those species for which the gellan gum environment significantly inhibited root growth. We have now used it to study RSA in sorghum, maize, soybean (Glycine max L.), canola (Brassica napus), and cucumber (Cucumis sativus). Second, we now do not need to grow and image the root systems in a glass cylinder, which improves image quality, as imaging through the glass water-filled tank and the glass cylinder introduced two sections of glass into the optical pathway. We can now simply grow the plants in a tank holding aerated hydroponic solution and multiple plants in their mesh towers, and then transfer individual towers with plant from the growth tank to the imaging tank for root imaging, and subsequently return them to the growth tank. Third, we now can grow older plants, with more developed and larger root systems enabling us, for example, to image more fully developed crown roots on sorghum and other cereal plants. Finally, because we are growing the plants in hydroponic media, we can now easily bring about changes in the composition of the root system, such as imposition of N, P, or K deficiency, or introduction of a toxic metal, such as Na, Cd, or Al. An example of using these root phenotyping platforms to study crop P efficiency came from our work by Hufnagel et al.82 using them to phenotype under low-P conditions a 243 line subset of a sorghum association panel (described by Casa et al.)154 consisting of both tropical converted and breeding accessions. The RootReader 3D platform was used to image and reconstruct 3D root systems from sorghum plants grown hydroponically in the plastic mesh system. The association panel was also phenotyped for 2D root morphology/topology traits using a RootReader 2D platform,155 as well as P uptake and root and shoot biomass for plants grown hydroponically in paper pouches, and for grain yield in the field on low-P soil (P efficiency). The root morphology/topology and RSA traits, and P uptake and P efficiency data were used to carry out a P efficiency candidate gene association analysis study with a group of sorghum genes that are homologs of the rice P efficiency gene, PSTOL1. As described earlier in the section “Molecular Basis of Root System Architecture as a Mechanism Enhancing Phosphorus Acquisition,” rice PSTOL1 is a receptor-like cytoplasmic kinase that is responsible for a major quantitative trait locus for rice P deficiency tolerance apparently via alterations in RSA resulting in a larger root system.19 The Hufnagel et al.82 study showed that multiple sorghum homologs of rice PSTOL1

 NOVEL ROOT SYSTEM IMAGING METHODS

139

FIGURE 7.1  Ten Days Old Sorghum Plant Grown Hydroponically in Plastic Mesh System Used for RootReader 3D Analysis

appear to play a fairly broad role in modifying RSA, not only enhancing root morphology traits but also changing RSA, which leads to increases in grain yield on low-P acid soils. These findings may have significant potential for using marker-assisted breeding to improve sorghum production and food security in the large areas of acid soils that exist in developing countries worldwide.

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CONCLUSIONS Examples of genes enhancing P uptake by the plants via changes in RSA and root morphology, such as PSTOL1 in rice and in sorghum are still rare. However, there are pieces of information in different published studies suggesting that some genes may act to enable cross talk between hormone pathways, P responses and root development, which can be manipulated to enhance crop performance on low-P soils. Although miRNAs are clearly associated with plant P responses, a direct role of these molecules in root morphology changes as an adaptive mechanism to P deficiency is still unclear. However, a detailed analysis of miRNA/target responses to P with regards to changes in root morphology does suggest these molecules are potential players in mechanisms acting to enhance P acquisition on high P-fixing soils. A direct link between these molecular determinants and crop performance under low-P availability is still missing, primarily due to the lack of experimental approaches deliberately designed to bridge this gap. Novel root imaging techniques that can be potentially scaled up in throughput and be employed with plants grown in soil are now being developed. With these methods, molecular genetic strategies, such as genome wide association mapping can soon be integrated into the wealth of functional information described here, giving rise to hypothesis-driven approaches to identify mechanisms enhancing P uptake for crops grown under low-P availability in the soil.

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CHAPTER

POTASSIUM SENSING, SIGNALING, AND TRANSPORT: TOWARD IMPROVED POTASSIUM USE EFFICIENCY IN PLANTS

8 Ryoung Shin

RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan

INTRODUCTION Potassium (K+) is the most abundant ion in plant cells and one of most crucial macronutrients. K+ plays roles in regulating various processes, including turgor pressure, ion homeostasis, enzyme activities, membrane polarity, and phloem loading of sugar.1–8 In addition, many fundamental biological processes in plants, such as photosynthesis, photorespiration, and cell growth, are controlled by K+ availability.9–11 Therefore, sufficient K+ supply is an essential factor in achieving optimal crop yield and quality. In developed countries, large volumes of fertilizers have been applied to agricultural fields in order to increase crop yield, but a large portion of the fertilizer remains in forms unavailable to the plants and leads to excessive application of fertilizers often resulting in soil and water pollution without positive effects on crop yield. In developing countries, lack of fertilizer application has resulted in insufficient crop production.12,13 Therefore, improved efficiency of K+ use would help to reduce agricultural production costs, to protect the environment and to increase crop yields. This chapter discusses ways to improve the K+ use efficiency in plants through understanding K+ transport mechanisms and regulation, and the responses of the plant to K+ availability.

POTASSIUM TRANSPORT MECHANISMS In plants, K+ is the most abundant essential macronutrient its concentration is maintained around 100 mM in the cytosol and between 10 and 200 mM in the vacuole.7,14,15 In order to sustain an appropriate concentration of K+, plants sense the K+ availability in their roots and turn on the K+ uptake machineries upon the K+ concentration. The identities of the K+ sensor and the various types of K+ channels and transporters involved in K+ movement in various tissues, and cells as of yet remain unknown.1,2,6,16–30 Epstein et al. proved that plants have dual K+ affinity uptake systems. One is a highaffinity K+ uptake system that functions in K+-limited conditions and the other is a low-affinity K+ uptake system that functions in K+-sufficient conditions.31 The shaker-type inwardly rectifying K+ channels are major players in low-affinity K+ uptake during K+ sufficient conditions. In Arabidopsis, the majority of low-affinity K+ uptake is performed by the Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00008-9 Copyright © 2017 Elsevier Inc. All rights reserved.

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shaker-type inwardly rectifying K+ channel ARABIDOPSIS K+ TRANSPORTER1 (AKT1), which is expressed in the plasma membranes of cells in the outer layers of roots.32–35 Other plant species have the same type of inward K+ channels, such as the rice OsAKT1, the grapevine VvK1.1, the barley HvAKT1, and the tomato LKT1.36–40 It is well-known that ion channels and transporters make complexes with other ion channels and that the various combinations of these complexes are able to respond to the wide range of K+ conductivity levels.41 In Arabidopsis, a shaker-type inward K+ channel, AtKC1, combines with other inward channels, including AKT1, K+ CHANNEL IN ARABIDOPSIS THALIANA1 (AtKAT1), AtKAT2, and AKT2/3.42–44 Although AtKC1 alone is known to not function as an inward K+ channel, it is required for the activation of K+ channel current and K+ uptake. Interestingly, the AKT1– AtKC1 complex has different current-voltage characteristics than the AKT1 homotetramer complex, which is not an efficient form of inward K+ channel.32,42–44 In addition to formation of the heterotetrameric AKT1–AtKC1 complex, the phosphorylation of AKT1 by the CALCINEURIN B-LIKE protein (CBL) INTERACTING PROTEIN KINASE 23 (AtCIPK23), in association with CBLs, is required for activation of this channel complex.42–45 Another essential level of regulation for AtKC1 is the interaction with the N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins.42–46 Other Arabidopsis shaker-type K+ channels are more involved in long-distance or intercellular K+ transport. An Arabidopsis shaker-type outwardly rectifying K+ channel, STELAR K+ OUTWARD RECTIFIER (AtSKOR), plays a major role in K+ transport from the stellar cell into the xylem in roots.26 This is the first step in long-distance K+ transport from roots to shoots, and the expression of AtSKOR is negatively regulated by abscisic acid.47 The Arabidopsis shaker-type inward K+ channels, AKT2/3 and AtKAT2, both participate in K+ phloem loading, but in different manners. AtKAT2, the expression of which is induced by auxin, is involved in K+ homeostasis in the phloem.48 AtKAT2 is also expressed in guard cell along with AtKAT1. AtKAT1 and AtKAT2 form a heterotetramer and are involved in the regulation of guard cell movement. The hyperpolarization of the guard cell membrane by H+-ATPase activates the K+ channels, such as the AtKAT1–AtKAT2 complex, resulting in opening of the stomata via increasing the K+ influx.28,48,49 However, the depolarization of the guard cell membrane results in increased K+ efflux via activation of K+ outwardly rectifying channels, such as Arabidopsis GUARD CELL OUTWARD RECTIFIER (AtGORK).50,51 In rice, one of the AtKAT1 homologues, the inward K+ channel OsKAT2, interacts with OsKAT3—but this interaction leads to negative regulation of the activity of OsKAT2.50,51 AKT2/3, which is regulated by calcium and extracellular proton levels, modulates the sugar loading into phloem via alteration of phloem potential.52–54 The H+:K+ symporters, the KT/KUP/HAK transporters, also play major roles as high-affinity and/ or low-affinity K+ transporters in plants.24 The first KT/KUP/HAK transporter was identified from barley, HvHAK1, the expression of which is induced by K+ deprivation and functions as a high-affinity K+ transporter.55 Most higher plants have multiple members of the KT/KUP/HAK transporter gene family in their genomes. For example, Arabidopsis has 13 KT/KUP/HAKs, and rice has 17.1,16,29,56,57 Arabidopsis HIGH AFFINITY K+ TRANSPORTER5 (AtHAK5) is one of most studied high-affinity K+ transporters, which is induced by K+ starvation and is responsible for K+ uptake from the root when K+ concentration is low.58–63 Nitrogen and phosphorus deprivation also resulted with the induction of AtHAK5 expression, however low K+ signal was required for the activation of AtHAK5-mediated high-affinity K+ uptake. This data suggests that the posttranscriptional regulation resulting from low K+ conditions is necessary for regulation of high-affinity K+ uptake.63 Recently, two groups showed that CBL-interacting kinase CIPK23 also activates the high-affinity K+ transporters from Arabidopsis (AtHAK5) and Venus flytrap (Dionaea muscipula, DmHAK5) in the same way of inward K+ channels (AKT1 and DmKT1), which are activated, by CBL-interacting kinases.

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Another H+:K+ symporter, Arabidopsis K+ UPTAKE PERMEASE1 (AtKUP1), functions across the broad-range of K+ available in soils and is considered a dual-affinity (low-affinity and highaffinity) K+ transporter.23 AtKUP2 is involved in cell expansion of growing cells,64 and AtKUP4 functions as a high-affinity K+ transporter and regulates root hair elongation.65 The Arabidopsis mutant of the plasma membrane-localized AtKUP7 recently showed a reduction in K+ uptake rate and K+ content in shoots under low K+ condition, a consequence of impaired K+ xylem loading.66 AtKUP2, AtKUP6, and AtKUP8 are involved in the regulation of turgor-dependent growth and ABA-mediated stomatal closing. The loss of these K+ transporters resulted in reduced survival rate in drought condition.67 Although all of these transporters are membrane proteins, the shaker-type K+ rectifying channels and the KT/KUP/HAK transporters discussed earlier have different characteristics. The KT/KUP/HAK transporters have a low selective preference between the alkali metals, which include not only K+ but also Na+, Rb+, and Cs+. This means that KT/KUP/HAK transporters can mediate transport of not only K+ but also Na+, Rb+, and Cs+.1,24 However, the shaker-type K+ channels better discriminate between the ions and more selectively transport K+ than other cations. Another big difference between the proteins in these two families is whether their activities are inhibited by ammonium (NH +4 ) or not. The shakertype K+ channel AKT1 is NH +4 -insensitive but a high-affinity K+ transporter, AtHAK5 is NH +4 -sensitive.13,62,68 The activity of AtKUP7 is also inhibited by NH +4 in yeast.66 A third class of K+ transporters are the HIGH-AFFINITY K+ TRANSPORTERs (HKTs). These are H+;Na+/K+ symporters found in all plant species. However, there are big differences between the HKTs in monocotyledonous and dicotyledonous plants.22 Although HKTs have been identified as K+ transporters, only the Na+ transporter activity has been confirmed for the dicotyledonous HKTs. On the other hand, the monocotyledonous HKTs have been shown to function as both K+ and Na+ transporters. Interestingly, substitution of a critical amino acid (serine to glycine) in the dicotyledonous HKTs enabled them to function as K+ transporters as well as Na+ transporters.22,69–79 The K+ EFFLUX ANITPORTERs (KEAs) are H+;K+ antiporters. The KEAs mediate K+ efflux into xylem.1,80,81 An Arabidopsis KEA, AtKEA2, controls cation and pH homeostasis in the chloroplast.82 TANDEM-PORE K+ channels (TPKs, previously called KCO channels) are involved in K+ movement in the vacuole. An Arabidopsis TPK channel, AtTPK1, is activated by Ca2+ and functions as an outward K+ channel. AtTPK4 mediates pollen membrane potential in a Ca2+-dependent manner.21,83–86 There are two families of nonselective cation channels that affect K+ transport. CYCLIC NUCLEOTIDE-GATED CHANNELs (CNGCs) are known to be nonselective cation channels, and also mediate K+ in various tissues.87,88 There are 20 CNGC genes in the Arabidopsis genome, of which AtCNGC1, AtCNGC2, AtCNGC4, AtCNGC10, AtCNGC16, and AtCNGC18 have been confirmed to transport K+ in plants or heterologous systems.89 GLUTAMATE RECEPTORs (GLRs) are also part of a nonselective cation channel family and are controlled by external amino acids, mainly glutamate. In Arabidopsis, two GLRs, AtGLR1.1 and AtGLR1.4, mediate transport of cations including K+.90–93 Some Na+ transporters also play roles in K+ transport in plants. Plant CATION/H+ EXCHANGERs (CHXs) were known to be Na+ transporters and regulate the homeostasis and osmolality in plants cells. Twenty-eight CHXs were found in the Arabidopsis genome, among which AtCHX13, AtCHX17, AtCHX20, AtCHX21, and AtCHX23 have been confirmed to have K+ uptake abilities.94–97 Particularly, AtCHX13 is transcriptionally upregulated by K+ deficiency. Mutation of AtCHX13 produced a plant showing a growth defect in K+ limited condition.97 In addition, two moss CHXs, PpCHX1, and PpCHX2, function as K+/H+ antiporters.98

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N+/H+ EXCHANGERs (NHXs) have been known to function in sequestration of Na+ into vacuoles. Some NHXs have been shown to sequester K+ as well.99–104 Among them, an Arabidopsis NHX, AtNHX3, dominates K+ sequestration, but is required for K+ deficient tolerance in Arabidopsis.105 A novel transporter has also been described from wheat. The LOW-AFFINITY TRANSPORTER1 (LCT) TvLCT1 has been confirmed to mediate K+ transport in yeast. TvLCT1 also transports other cations, such as Na+, Ca2+, and Cd2+, in yeast.106–108 Although many K+ channels and transporters have been characterized, there are still many membrane proteins predicted to be involved in K+ transport based on sequence homology. These predicted proteins, particularly in crop plants, await functional confirmation in vivo. Further understanding of the K+ channels and transporters available in plants is necessary for crop improvement.

REGULATORY COMPONENTS REGULATORY COMPONENTS OF K+ TRANSPORT Ca2+ is one of the major regulatory components of K+ transport in plants.37,52,109–118 In particular, some of the Ca2+sensor complexes, comprised of CBLs and CIPKs, play critical roles in regulating K+ channels and K+ transporters.37,52,114,115,119–121 The phosphorylation of an Arabidopsis inward K+ channel, AKT1 by the AtCIPK23–AtCBL1/9 complex is a prerequisite for the activation of AKT1.34,35,119,122,123 Another Arabidopsis inward K+ channel, AKT2/3, is also activated following phosphorylation by the similar complex, AtCIPK6–AtCBL4.52 Recently, the phosphorylation of the Arabidopsis K+ transporter AtHAK5 by AtCIPK23 was confirmed.121 Multiple Arabidopsis K+ channels also interact with an Arabidopsis PROTEIN PHOSPHATASE 2C (AtPP2C), an interaction that results in the dephosphorylation and inactivation of the K+ channel.119,124 Arabidopsis SNARE proteins mediate the vesicle trafficking response to stresses. An Arabidopsis SNARE protein, AtSYP121, interacts with the modulating channel subunit AtKC1, resulting in the regulation of the K+ channel AKT1.125–127 The phosphoprotein-binding 14-3-3 proteins are involved in multiple biological processes. K+ limitation results in transcriptionally regulated increase in Arabidopsis 14-3-3 proteins.128–130 In addition, 14-3-3 proteins positively regulate AtKAT1131,132 and the vacuolar K+ channel TPK183,85,133 via protein–protein interaction.

REGULATORY COMPONENTS OF K+ DEFICIENCY SIGNALING Plants must have strategies to overcome K+ limitation in order to survive and grow efficiently. Although any growth phenotypic alteration in response to K+ limitation takes a few days, the early responses happen within a few hours, resulting in the activation of the high-affinity K+ uptake system.1,6,13,134,135 Ca2+ and REACTIVE OXYGEN SPECIES (ROS), produced via NICOTINAMIDE ADENINE DINUCLEOTIDE PHOSPHATE (NADPH) Oxidases, and the phytohormone ethylene work together as the major signaling components in Arabidopsis.1,13,59,63,112,121,135–138 As described earlier, Ca2+ is an essential component of the control over K+ channels and transporters. Ca2+ is also involved in the downstream signaling after plants sense the K+ status. Ethylene is induced in plants while ROS is induced in roots. One NADPH oxidase, RESPIRATORY BURST OXIDASE HOMOLOG C (AtrbohC)/ROOT HAIR DEFECTIVE 2 (AtRHD2) plays critical roles in K+ deficiency signaling, such as altering gene expression and root hair growth, as well as activating the high-affinity K+ uptake system.135,138,139 In Arabidopsis experiencing low K+ conditions, atrhd2 mutants fail to establish the high Ca2+ gradient and

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fail to accumulate ROS in root hair tips, resulting in defective root hair elongation.139 An Arabidopsis endoplasmic reticulum-localized peroxidase, RARE COLD INDUCED GENE 3 (AtRCI3), works with AtRHD2 to produce ROS that then signals the upregulation of AtHAK5 expression in K+ deficient condition.135,137,138 The ROS-induced AtHAK5 expression also requires ethylene as an upstream component in K+ deficient condition.136 Much like AtHAK5, the induction of many other K+ deficiencyresponsive genes depends on ethylene and ROS in Arabidopsis.134–136,138 Downstream of this signaling, an Arabidopsis AP2/ERF-type transcription factor, AtRAP2.11 was isolated via activation tagging of the AtHAK5 promoter fused with a luciferase reporter under K+ deficiency. AtRAP2.11 is activated by ethylene and ROS, and binds a GCC-box of the AtHAK5 promoter to regulate the expression of AtHAK5.140 In addition to ethylene, the phytohormones, such as jasmonic acid, auxin, cytokinin, and ABA are also regulatory components in K+ deficiency signaling.1,13,134,141–145 Armengaud et al. showed that K+ deficiency leads to increased jasmonic acid production and subsequent alteration in expression of a set of genes. Several genes involved in jasmonic acid biosynthesis are induced by K+ starvation,141 resulting in increased levels of jasmonic acid via the receptor of jasmonic acid, CORONATE-INSENSITIVE 1 (COI1).141,142 Auxin is involved in the modulation of lateral root growth under K+ deficiency. When Arabidopsis is starved for K+, both the number of lateral roots and the length of the lateral roots were dramatically decreased, processes which are both regulated by auxin. It has been shown that K+ starvation reduces the level of free indole-3-acetic acid, which leads to downregulation of the expression of the Arabidopsis r2r3-type myb transcription factor AtMYB77, which in turn reduces lateral root growth.145 Auxin also modulates some inward K+ channels.48,146 Furthermore, AtKUP4 has been implied in auxin transport.65,147 The level of cytokinins is also decreased during K+ deficiency. This decreased level of cytokinins and cytokinin signaling affects K+ deficiency-induced gene expression, ROS accumulation, and root hair growth.144 Thus far, these four phytohormones are respond to low K+ levels in a way that stimulates K+ uptake. On the other hand, ABA shows a different response to K+ deficient and has a different role in K+ deficiency signaling. In early stages of K+-limited condition, plants probably confuse this state as a high Na+ condition, which leads to increased ABA production. K+ deficiency inhibits a negative regulator of ABA signaling, the nuclear factor NUCLEO PROTEIN X1 (AtNPX1) in Arabidopsis.143 ABA was also shown to regulate Arabidopsis K+ channels, such as AtGORK and AtKAT1.148,149 It is still unclear what, if any, roles of phytohormones not mentioned in this section play in the regulation of K+ sensing and response signaling.

STRATEGIES TO IMPROVE K USE EFFICIENCY IN PLANTS K+ is an essential mineral nutrient and one of earth’s most abundant mineral components, at 2.5% of the lithosphere. Unlike nitrogen, phosphorus, and sulfur, other macronutrients, K+ is not metabolized, and there is no source, or forms therein, other than the K in soils and rocks.13 Although K is an abundant mineral, over the last 30 years improved plant growth and crop yield has required a more than 25% increase in K fertilizer use.150 Most of the K in soils exists in the forms which is not available to plants, and other soil components, including H2O, obstruct plant K+ uptake from soils.13,19,150,151 Therefore, plants recognize a case of K+ limitation, not because of little K in soils, but because of the limited

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amounts of exchangeable forms of K, despite the large portions of K existing as nonexchangeable and structural forms.150 In this section, potential strategies that can be employed to improve K use efficiency are discussed, with a focus on ways to increase K availability, root surface area, and K+ transport in plants (Fig. 8.1).

FIGURE 8.1  Summary of Strategies for Improving K Use Efficiency in Arabidopsis

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INCREASING K AVAILABILITY IN PLANTS The conversion of the nonexchangeable K to exchangeable K is one potential strategy to increase K availability in the soil.152,153 The nonexchangeable K could be released by inoculation of the soil with K-solubilizing microorganisms. It has been shown that inoculations of Bacillus mucilaginosus in fields for pepper, cucumber, and sudan grass, inoculation of Bacillus edaphicus for wheat, and coinoculation of Bacillus coagulans and Bacillus megaterium produce a positive effect on increasing K availability.152,154–156 Additionally, the inoculation of Bacillus edaphicus accelerated K mobility in soils via production of organic acids, such as oxalic acid, citric acid, tartaric acid, succinic acid, and α-ketogluconic acid,154,157 which help to convert the nonexchangeable K to exchangeable K. In a similar strategy, plants release organic acids as root exudates, which leads to increased K+ availability in soils.13,158 Furthermore, the amino acids present in root exudates of wheat has positive effects on K mobilization.153 Thus, establishing a large population of beneficial microflora, perhaps even those selected or engineered for metabolic specialties, is one strategy that could increase the amount of soil-borne K+ available to plants.

INCREASED PLANT ROOT SURFACE TO SECURE GREATER ACCESS TO K IN SOILS Plants absorb K via root cells. When Arabidopsis plants are starved for K for more than a day, root hair elongation occurs.136 Barley, red clover, alfalfa, pea, rye, ryegrass, and rape also show elongation of root hairs in response to K starvation.13,159 A larger root surface area would enhance the plant’s access to K and increase the chances for K+ uptake. Increases in root hair surface area could be acquired by modification of root hair development, possibly through the overexpression of involved genes. Plants overexpressing the transcription factor AtRAP2.11 showed higher expression of ethylene responsive genes and increased ROS production in roots. The RAP2.11 protein binds the AtHAK5 promoter, and its overexpression resulted in increasing AtHAK5 transcription even under sufficient K levels. Under K-limited condition, AtRAP2.11 overexpression produced plants with a greater number of root hairs and that grew better than wild-type.140 Several genes are known to affect root hair growth. The overexpression of the Arabidopsis MYBlike transcription factor CAPRICE (AtCPC1), a positive regulator of root hair differentiation, or of the tomato CPC-like MYB transcription factor SlTRY enhanced root hair growth.160–162 The overexpression of a cotton bHLH-type TCP transcription factor in Arabidopsis enhanced root hair initiation and elongation as well.163 In addition to targeting the number and length of root hairs, enhancement of primary and lateral root volumes could positively affect K uptake. Several Arabidopsis transcription factors, including AtMYB77, NO APICAL MERISTEM CUP-SHAPED COTYLEDON1 (AtNAC1)164,168,169, KNOTTED-LIKE ARABIDOPSIS TRANSCRIPTION FACTOR 6 (AtKNAT6), and the MADS box transcription factor AtANR1, are positive regulators of root growth.13,145,164–167 However, the constitutive overexpression of transcription factor often has negative effects on plants growth. Therefore, expression of these root growth-related transcription factors under root specific and/or K+ deficiency-induced promoters, such as an AtHAK5 promoter, could be a viable strategy to improve K+ uptake efficiency.60

IMPROVE THE EFFICIENCY OF K+ UPTAKE AND TRANSLOCATION IN PLANTA The most direct approach to enhance K+ use efficiency is to improve K+ uptake and translocation. In many cases, overexpression of single K+ channel or transporter has not been successful in improving K+ uptake in plants.13,58,168 This is probably due to the redundancy and/or complex regulation of

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these membrane proteins. However, there are some reports that overexpression of transcription factors that activate AtHAK5 resulted in increased tolerance to K+ starvation.60,140 Therefore, modification of upstream components regulating K+ uptake or translocation may be a more efficient way to improve K+ use efficiency in plants. However, the majority of these upstream components have not yet been revealed, so more time is required to make this approach feasible. Crop breeding may be one way to improve K+ uptake and use efficiency in plants. There are several known quantitative trait locus (QTL) in Arabidopsis thaliana and Brassica oleracea that are associated with shoot K+ accumulation. One segment on Arabidopsis chromosome four includes AtKUP2, ATK2, AtKAT2, and AtTPK3.169,170 Also, the ploidy status has an influence on the efficiency of K+ accumulation in plants. Tetraploid Arabidopsis shows higher K+ accumulation than diploid or haploid Arabidopsis,171 suggesting that ploidy status, possibly through the copy number of a select set of genes, could be a factor used to improve K+ use efficiency in plants.

CONCLUSIONS The efficiency with which plants can acquire available K+ is directly connected to the quality, rate, and yield of plant growth. Therefore, improving K+ use efficiency in plants is one critical point in the consideration of development of new crop cultivars. In this chapter, the current knowledge regarding K+ transport mechanisms and their regulation and K+ deficiency signaling are summarized. Based on the relevant information, three strategies to improve K+ use efficiency in plants are suggested. The first suggested strategy centers on increasing K+ availability through the inoculation of the soil with Bacillus spp. and through the alteration and amplification of root exudates in order to increase the modification of nonexchangeable K to exchangeable K. The second strategy focuses on enhancement of root surface area in order to improve the chances of the plant to access the K in the soil. The third strategy relies on improving K+ uptake and translocation in plants via directly or indirectly activating K+ transport machineries. Understanding the current knowledgebase, seeking greater understanding of the existing mechanisms, and incorporating the suggested strategies into plant breeding and engineering programs will contribute to improved K+ use efficiency in plants.

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CHAPTER

UNDERSTANDING CALCIUM TRANSPORT AND SIGNALING, AND ITS USE EFFICIENCY IN VASCULAR PLANTS

9

Agustín González-Fontes, María T. Navarro-Gochicoa, Carlos J. Ceacero, María B. Herrera-Rodríguez, Juan J. Camacho-Cristóbal, Jesús Rexach University Pablo de Olavide, Sevilla, Spain

INTRODUCTION Calcium (Ca) is an essential nutrient for the suitable development of vascular plants, as it plays crucial structural and signaling roles. Calcium is required for cell wall and membrane stabilization, as a counterion for inorganic and organic anions, and as a second messenger in multiple intracellular signaling events that control plant responses to biotic and abiotic stresses.1–3 Calcium concentration in plant tissues varies depending on factors, such as the growing conditions (soil, pH, and climatic factors), plant organ, age of the plant, or species.4 The relative distribution of total Ca is approximately 70%–90% in the leaves and 10%–30% in the root.5 Calcium levels in lowtranspiring organs, such as fruits and seeds are usually very low,5 whereas Ca concentration in mature transpiring leaves can reach 10% of the dry weight with an adequate Ca supply.3 Calcium is not uniformly distributed within plant tissues, and an important amount of the total Ca in plants is located in cell walls, where Ca can cross-link carboxyl groups of pectin chains in the middle lamella, which is necessary for cell wall integrity.6–8 It is assumed that dicots generally require more Ca for optimum growth than monocots, which has been attributed to their higher concentration of cell wall pectate.4,9,10 Calcium also plays a key role in membrane structure and function. Calcium is able to bridge phosphate and carboxylate groups of phospholipids and proteins—mainly at the external face of the plasma membrane—which stabilizes the lipid bilayers of cell membranes.3,7,11 Intracellular free Ca2+ concentrations—especially the cytosolic one—must be tightly controlled, as Ca2+ readily forms insoluble salts with phosphates.1 Free Ca2+ concentrations are spatially regulated within cells through transporters located on cell and organelle membranes with different affinities for Ca,12,13 so that the cytosolic Ca2+ concentrations vary between nanomolar and low micromolar levels during resting state and signaling events,14 whereas vacuolar Ca2+ concentration can reach millimolar values.5,15 Another consequence of this preferential vacuolar accumulation is that Ca2+ may contribute to osmotic balance by acting as a counterion for inorganic and organic anions, especially in vacuolated leaf cells.3,5 Calcium is an easily available element in nature and especially abundant in neutral and alkaline soils. However, it has to be added to acid soils to supply the required Ca for optimal plant growth. Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00009-0 Copyright © 2017 Elsevier Inc. All rights reserved.

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Calcium content in soils depends on factors, such as its parent material, degree of weathering, and whether Ca is added as fertilizer or liming.4 This element is present in the soil: (1) as a structural component of minerals having different degrees of solubility, (2) on the exchange complex, and (3) as a cation in soil solution.4,16 Exchangeable Ca—present in the soil in large quantities—is in equilibrium with soil solution Ca, and reported concentrations of Ca in the soil solutions are up to about 20 mM.17 The concentration of other cations in the external solution also affects Ca requirement for optimal plant growth because these cations easily replace Ca2+ from its binding sites at the plasma membrane, thereby increasing the leakage of ions and metabolites from cells. Accordingly, Ca concentrations required for optimal growth enhance with increasing external concentrations of heavy metals, Al, or Na.18–20 In addition to the role of Ca in plant nutrition, this macronutrient plays an important function in many cell signaling processes, being an important second messenger in signal transduction pathways. Plants respond to many environmental abiotic/biotic stimuli that trigger changes in the cytosolic Ca2+ concentration.2,12,13 A stimulus can trigger the opening of Ca2+-permeable channels in the plasma membrane, reticulum endoplasmic membrane, or tonoplast, leading to Ca2+ influx into the cytosol. The elevation in cytosolic Ca2+ concentration is terminated by Ca2+ ATPases and H+/Ca2+ antiporters that restore cytosolic Ca2+ to its resting level. This process can be repeated over time, producing temporal oscillations of cytosolic Ca2+. These temporal and spatial variations in the cytosolic Ca2+ concentration are commonly designated as Ca signatures.2,12,13 In this chapter, several topics related to Ca physiology are addressed, such as its deficiency, uptake and distribution, and calcium use efficiency in vascular plants. Special attention is paid to Ca2+ channels, H+/Ca2+ antiporters, Ca2+ ATPases, and other Ca2+-related proteins due to their significance in Ca homeostasis and signaling pathways.

CALCIUM DEFICIENCY IN PLANTS Calcium deficiency may occur in soils with low base saturation or high acid content.21 In addition, severe weathering and leaching of soils may lead to deficiency in Ca.4,11 In soils with insufficient Ca levels, the yield and quality of crops can be decreased, resulting in costly economic losses.1 Deficiency symptoms first appear in the newly emerging leaves or developing tissues, while older leaves may contain sufficient concentrations of Ca.4 This occurs because Ca cannot be mobilized from older tissues and redistributed through phloem due to its relatively high phloem immobility. It has been proposed that Ca is a rather phloem-immobile cation, owing to its inability to enter the sieve elements.22 Calcium is supplied to developing tissues in the xylem by the transpiration stream, which is low in young leaves, in enclosed tissues, and in fruits.1 Hence, Ca deficiency is frequently present in tissues with low transpiration rates when compared to other parts of the plant, which shows the key role of transpiration in Ca allocation.15 However, even with a high leaf transpiration rate, Ca deficiency can also occur in adjacent organs of the same plant that slowly transpire, such as fruits.15 Calcium deficiency affects the development of cell wall,1 and is characterized by necrosis of young meristematic regions, such as the tips of roots and young leaves or developing tissues.4 In addition, under Ca deficiency the young leaves may also appear curved and sticky or gummy to the touch, the root becomes brown and a decrease in lateral branch number is observed.4 Shoot damage is typically higher than in the root, which leads to a lower shoot–root ratio in Ca-deficiency plants.23 To alleviate

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many of these symptoms of Ca deficiency, crop plants can be treated with supplemental soil or foliar Ca sources even though this is not always effective.15

CALCIUM UPTAKE AND DISTRIBUTION Due to its key role in plant signaling and structure, the regulation of Ca uptake and distribution is crucial for physiological functions and for preventing Ca deficiency.3,14

CALCIUM UPTAKE BY ROOTS AND DELIVERY TO THE XYLEM In soil water solution, Ca is in the form of a divalent cation (Ca2+) and its entry into the root is strongly favored by the electrochemical potential gradient. Calcium is taken up by the roots and this cation can traverse them either through the cytoplasm of cells connected by plasmodesmata (the symplast) or through the spaces among cells (the apoplast). The movement of Ca through these pathways is well balanced to allow a low enough cytosolic Ca2+ concentration in the root cells, to regulate the rate of Ca to the xylem, and to prevent the accumulation of toxic cations in the shoot.1,3,8 Once taken up, Ca along with water can follow apoplastic or symplastic pathways to the xylem. Similar to other ions, the impermeable Casparian strip surrounding the root endodermis restrains the apoplastic route of Ca2+ across the endodermis into the xylem. Thus, movement of Ca2+ across the endodermis is necessarily by the symplastic pathway. Calcium enters the cytosol of endodermal cells via Ca2+ channel located on the cortical side of these cells, and Ca2+ is actively released across the endodermis plasmalemma in the stele by Ca2+ ATPases.5,8 The Ca apoplastic flux depends on the rate of transpiration, whereas the symplastic pathway allows the control of the rate and selectivity of Ca2+ transport to the shoot depending on the demand for this macronutrient.1,24 The respective contributions of the apoplastic and symplastic routes in the Ca2+ delivery to the xylem are unclear in most plants, but appears to depend on the species.5,25

CALCIUM TRANSPORT TO THE SHOOT Calcium is transported through the plant mainly via the apoplastic route and delivered to the shoot through the xylem, this process being significantly influenced by the transpiration rate.1 Once in the shoot via the transpiration stream, Ca2+ transport largely follows the apoplastic pathway.26 This cation accumulates in mesophyll cells, trichomes, or epidermal cells,27 being located in vacuoles for long-term storage.1,5 Tonoplast Ca2+ transporters are essential for the storage of this cation in leaf vacuoles, and a high transpiration rate contributes to this process by increasing the Ca2+ movement through the apoplastic route. Accordingly, Ca accumulates at higher concentrations in actively transpiring organs. By contrast, lower transpiration rates decrease the leaf Ca accumulation, and the inhibition of transpiration prevents the storage of Ca in leaves.5,26 Calcium enters the leaf cells via Ca2+ channels localized to their plasma membranes. Once again, a significant electrochemical potential gradient ensures passive Ca2+ uptake into the cytosol from the apoplast, cytosolic Ca2+ concentration being sensitive to the apoplastic Ca2+ concentration.8 In leaves, apoplastic free Ca2+ concentration is kept under about 50 µM by either cellular Ca2+ uptake, formation of complexes with cell wall pectate, or accumulation in trichomes.5,28 Moreover, a high cytosolic Ca2+

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concentration is toxic, hence its concentration must be kept at a very low level in unstimulated cells by Ca2+ ATPase and H+/Ca2+ antiporters, which withdraw cytosolic Ca2+ either to the apoplast or organelles, such as the vacuole or the endoplasmic reticulum.29,30 In leaves, Ca2+ is usually deposited in vacuoles where it can reach levels higher than 150 mM.1,31 The leaf vacuole is widely regarded as the main Ca2+ store in plant tissues, but unlike the cytosol, the vacuolar Ca2+ concentration varies notably among cell types.1,5 In fact, total vacuolar Ca2+ concentration in cereals is significantly higher in leaf epidermal cells (up to 150 mM31) than in mesophyll vacuoles (lower than 10 mM), and in most eudicots studied the vacuolar Ca2+ concentration is low in epidermal and bundle sheath cells (lower than 10 mM), but higher than 60 mM in mesophyll cells.5,32 Consistent with this, eudicot Arabidopsis mesophyll cells have a higher capacity to store Ca in their vacuoles than epidermal cells, owing to the more pronounced presence of CAX1 (a H+/Ca2+ antiporter) in the tonoplast of mesophyll vacuoles.8,32 Once Ca is located in the leaf vacuole, only very small amounts of this cation are redistributed in signaling events, most intracellular Ca remaining relatively immobile,1 as explained before.22

CALCIUM AS A SIGNAL In plant signaling pathways, the participation of several proteins capable of transporting Ca2+, including Ca2+-permeable channels, divalent H+ antiporters, and Ca2+-P–type ATPases, is essential.14 Channels move Ca2+ in the cytosol passively along its electrochemical potential gradient, while only transporters located mainly in the tonoplast and plasmalemma are capable of accumulating Ca2+ against its electrochemical potential gradient into vacuoles and apoplast, respectively.14 Channel and transporter activities must be tightly modulated to generate an adequate Ca signature and, consequently, the cytosolic Ca2+ concentration is highly regulated. When the cell is in its resting state, the cytosolic Ca2+ concentration is about 100 nM, but may be higher than 1 µM for signaling events.1,2

CHANNELS INVOLVED IN CALCIUM INFLUX AND SIGNALING Calcium channels have been described in all plant membranes. They have been classified according to their voltage dependence into hyperpolarization-activated (HACC), depolarization-activated (DACC), and voltage-independent (VICC) cation channels.1,3,12 HACCs play a key role in stomatal closure during water stress. The increase in ABA concentration stimulates the opening of HACCs, thereby promoting the entry of Ca2+ that depolarizes the plasma membrane and triggers Ca2+-dependent events, resulting in the stomatal closure.33,34 DACCs are plasma membrane–located channels and have permeability to both mono- and divalent cations. These channels participate in the uptake of Ca2+, as well as of other cations. Interestingly, K+ outward-rectifying channels (KORCs) are also Ca2+-permeable DACCs.1 DACCs have also been involved in the acclimatory responses of plants to low-temperature stress.35 Many different VICCs are present in the plant plasma membrane, which differ principally in cation selectivity. In contrast to HACCs, VICCs are permeable to both mono- and divalent cations.1,36 VICCs are encoded by genes from the cyclic nucleotide–gated channel (CNGC) and glutamate-like receptor families (GLR). They have a role in Ca2+ influx, and hence balance the permanent efflux of Ca2+ through Ca2+ ATPase and H+/Ca2+ antiporters, which maintain cytosolic Ca2+ homeostasis in resting plant cells.1,3

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The main Ca reservoirs are the apoplast and organelles, such as vacuole, endoplasmic reticulum, mitochondria, and plastids. Several membrane channels are involved in generating a Ca2+ influx from these compartments to cytosol, triggering Ca signatures.2 CNGCs and GLRs are two of the main Ca2+ channels. Evidence supports that most of protein channels encoded by Arabidopsis CNGC and GLR gene families are more involved in processes associated with Ca2+ signaling than with Ca2+ storage.5

Cyclic nucleotide–gated channels These channels are present in both monocots and dicots. In Arabidopsis thaliana, this family is constituted by 20 members.37,38 It is thought that all plant CNGCs consist of four subunits, each of which contains six transmembrane-spanning domains (S1–S6), a pore-forming loop (P loop) localized between S5 and S6, and separate cyclic nucleotide monophosphate–binding (CNBD) and calmodulin (CaM)binding domains (CaMBD).36,39,40 CNGC activity is regulated by the binding of cyclic nucleotides that triggers the opening of these channels, whereas Ca2+/CaM binding to the CaMBD provokes channel closure.41 Usually, CNGCs have been confined to the plasma membrane, but recently a different subcellular localization in A. thaliana and Medicago truncatula has been reported. For instance, AtCNGC19 and AtCNGC20 are localized in the tonoplast,42 whereas MtCNGC15 is in the nuclear envelope.43 CNGCs are nonselective cation channels involved in the transport of several mono- and divalent cations.44 In addition, CNGC isoforms have been implicated in changes of the cytosolic Ca2+ concentration triggered in response to abiotic stresses and during plant development regulation.45 For instance, boron deficiency increased the expression of AtCNGC19 in roots, and an increase in the cytosolic Ca2+ concentration was also observed under boron deprivation.46 AtCNGC14 is involved in growth inhibition induced by auxin and gravitropic bending in Arabidopsis roots, and this channel participates in auxin-induced Ca signaling pathway during the gravitropism response.47 In addition, it has been described that AtCNGC18 is essential for the Ca signature required for pollen tube guidance to ovules.48

Glutamate-like receptors

An increasing number of experimental results have indicated a crucial role of GLRs in Ca2+ influx into the cytosol, after their activation by respective ligands.49–51 These receptors have been identified in tomato, rice, and Raphanus sativus.51 In Arabidopsis, GLR family is composed of 20 genes.52 GLRs contain six domains: S1 and S2 are localized outside of cells and are ligand-binding sites capable of binding two different ligands; and M1–M4 constitute four transmembrane-spanning domains, which form a selective pore for the passage of cations.51 Although GLRs have been described as nonselective cation channels, experimental data obtained from their expression in heterologous systems point toward a strong preference for Ca.53–55 Four GLR subunits are probably required to form a glutamate receptor complex (GLR channel) in Arabidopsis.51 Homo- and heteromeric GLR channels have been reported.53,54 GLRs can link (through S1 and S2 domains) to a large number of different ligands, including several amino acids.51 Interestingly, the application of not only glutamate or glycine, but also asparagine, serine, and methionine, among others, leads to Ca2+ influx via GLR channels.53,55 It has been proposed that different amino acids could bind to a GLR subunit, depending on their structure and charge, which induce different Ca2+ currents.51,55 Plant GLRs are involved in many physiological events, such as stomatal movements, photosynthesis, carbon metabolism, root architecture, plant development, photo- and gravitropism, and Ca2+ homeostasis.51 Several abiotic stresses provoked an altered GLR gene expression in both Arabidopsis

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and rice.56,57 Interestingly, the transgenic overexpression of OsGLR1 and OsGLR2 led to an enhanced drought tolerance in rice and Arabidopsis when they were heterologously expressed.58

TRANSPORTERS INVOLVED IN CALCIUM EFFLUX AND SIGNALING Calcium withdrawal from the cytosol against its electrochemical potential gradient to either the apoplast or intracellular organelles depends on active transport, which is carried out by Ca2+ ATPases and H+/Ca2+ antiporters. These enzymes play several important roles,29,30 namely, they keep a sufficiently low Ca2+ concentration in the cytosol of resting cells compatible with life (in the nanomolar range); they restore cytosolic Ca2+ concentration to resting levels after a disturbance of cytosolic Ca2+ concentration, which influences the Ca signals; they replenish the intracellular and extracellular Ca stores for subsequent cytosolic Ca2+ signals; and they provide Ca in the endoplasmic reticulum.1,59 It has been proposed that Ca2+ ATPases, which have a high affinity (Km = 0.4–10 µM60) but low capacity for Ca2+ transport, are responsible for maintaining homeostasis of cytosolic Ca2+ concentration in the resting cell, while the H+/Ca2+ antiporters, which have lower affinities (Km = 10–15 µM) but a great capacity for Ca2+ transport, are likely to remove Ca2+ from the cytosol during Ca2+ signals and to modulate perturbations in cytosolic Ca2+ concentration.30 Therefore, H+/Ca2+ antiporters are especially important for the restoration of cytosolic Ca2+ levels associated with signaling pathways.29,61,62

Cation/H+ exchangers

Cation/H+ exchangers (CAXs) have been identified in both monocots and dicots, usually being present at an average of 5–6 CAX genes per species.62 Concerning the CAX structure, these transporters contain 11 transmembrane domains, one N-terminal transmembrane helix (MR), and a conserved core of 10 spanning membrane domains M1–M10. The cation-binding pocket is formed by M2/M3 and M7/M8, and specific glutamate residues in M2 and M7 have been proposed to bind Ca.63 CAXs are secondary energized Ca2+ transporters that use the proton-motive force produced by H+pumping ATPases and pyrophosphatases. The stoichiometry of the dominant H+/Ca2+ antiporter in the tonoplast is described as 3H+/1Ca2+.12,64 CAXs predominantly perform Ca2+ transport from the cytosol into vacuole, participating in Ca2+ signaling pathways.62 Upon the increase in the cytosolic Ca2+ concentration triggered by abiotic/biotic stimuli, Ca2+ has to be removed from the cytosol to restore resting cytosolic Ca2+ concentration. CAXs play a major role in this process, as their high capacity for Ca2+ transport allows the removal of cytosolic Ca2+ when the level of this cation is higher than its resting concentration.29,61 Six CAX genes have been described in Arabidopsis, and their encoded proteins involved in the vacuolar Ca2+ accumulation.59 AtCAX1 gene has a lower expression in roots than in leaves, which could explain the low root vacuolar Ca2+ concentration and the higher vacuolar Ca2+ accumulation in the mesophyll.5 In addition, the expression of many Arabidopsis and rice CAX genes was altered under several abiotic stress conditions, such as drought, heat, cold, or salinity.62 Regarding metal stresses, an overexpression of CAX genes may increase metal tolerance via metal vacuolar sequestration.65,66 However, it is unclear whether CAX are directly involved in metal ion transport or these transporters are associated with Ca signaling. A few years ago, it was suggested that CAXs could participate in Ca signaling pathway triggered by boron deficiency.46,67

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Autoinhibited Ca2+-ATPase proteins

Plant Ca2+-ATPase proteins are Ca2+-efflux transporters belonging to the second subclass (II) of phosphorylated (P)-type ATPases superfamily, and are included within type IIA and type IIB subgroups. They are involved in many physiological and developmental processes, such as vegetative growth, stomatal movement, or divalent cation transport, among others.68 Arabidopsis contains members of both subgroups of transporters, 4 type-IIA Ca2+ ATPases (which lack an N-terminal autoregulatory domain) and 10 type-IIB Ca2+ ATPases [which contain an N-terminal autoinhibitory domain (ACA or autoinhibited Ca2+ ATPase)].69 ACAs are characterized by an N-terminal autoinhibitory domain, which contains a binding site for CaMs (activation), and a serine phosphorylation site for calcium-dependent protein kinases (CDPKs) (inactivation).70,71 It is noteworthy that a distinctive feature of ACAs from the others P-type ATPases is the possible use of GTP or ITP as an alternative nucleotide triphosphate instead of ATP.72 The membrane location of ACA proteins in the plant cell is diverse. In addition to their presence in the plasma membrane (ACA8, ACA9, ACA10, and ACA12), they are also present at the endoplasmic reticulum (ACA2), vacuoles (ACA4 and ACA11), and plastid envelope (ACA1).69,73 Furthermore, vacuolar ACAs are found at different tonoplast locations: while ACA11 is situated in the central vacuole, ACA4 is present in small vacuoles.74 The profusely and diverse cell emplacements of ACAs support their involvement in the maintenance of Ca homeostasis at different levels. Moreover, it has been described that ACA gene expression is altered as a response to diverse stresses.45,46,75

CALCIUM SENSOR PROTEINS AND THEIR INVOLVEMENT IN PLANT STRESS RESPONSES The spatial and temporal changes in cytosolic Ca2+ concentration contain encrypted information (signal), which has to be perceived and transduced through proteins termed “Ca sensors” that are key elements in signaling processes in response to environmental changes. Plant Ca sensors, whose conformation or catalytic activity changes once Ca2+ is bound, are mainly classified into three groups, namely, CaMs and calmodulin-like proteins (CMLs), calcineurin B–like proteins (CBLs) and their interacting kinases (CIPKs), and CDPKs.76

Calmodulins and calmodulin-like proteins

CaMs and CMLs are Ca sensor proteins that contain EF-hand elements for Ca2+ binding. There are many members of this protein family in the plant genome, with the presence of 7 CaM and 50 CML genes in Arabidopsis. Although CaMs are highly conserved in eukaryotes, CMLs are principally present in plants and some protists.77 CaMs are located mainly in the cytosol, but they are also identified in the nuclei, plasma membrane, and several cell compartments. CaMs are essential proteins for sensing Ca signals in the nucleus, which contains a specific Ca-signaling mechanism different from that of the cytosol.77,78 In addition, there is a growing interest in knowing the role of the Ca2+/CaM complex in the control of gene expression for plant responses.78 This regulation would be performed through the posttranslational modification of transcription factors or, as described in animals, with the participation of CaM-binding protein kinases and CaM-binding protein phosphatases.79,80 CaM and CML proteins are classified as sensor relays without enzymatic function. Nevertheless, usually conformational changes occur upon binding Ca2+, and they interact with a broad range of target

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proteins mostly through hydrophobic interactions.77,81 The CaM/CML target proteins can include transcription factors, kinases, phosphatases, metabolic enzymes, ion channels, or transporters.82 It is widely described that CaM/CMLs act as components of Ca-signaling pathways in plant responses to abiotic stresses through the regulation of target proteins.45,82 CNGCs can also interact with CaM-binding proteins, as identified in Hordeum vulgare, Nicotiana tabacum, and A. thaliana.83 Furthermore, it is described that CaMs would regulate CNGC function positively and negatively through the involvement of several CaMBDs.83 In addition, the vacuolar Ca2+ ATPase AtACA4 of Arabidopsis is also CaM regulated, and is involved in tolerance mechanisms against osmotic stress.84

Calcineurin B–like proteins CBLs are Ca sensors that belong to the CaM family. They were initially identified due to their similarity to calcineurin B and neuronal Ca sensors from animals.85 Based on phylogenetic analysis they have been classified into three groups in Arabidopsis.86 There is a growing interest in analyzing the transduction mechanisms by which CBL proteins interact with CIPK for stress responses.87–89 It has been suggested that the increased number of CBL and CIPK genes in plant evolution would be explained as an adaptation process to environmental changes.77 CBL proteins have four EF-hands responsible for Ca2+ binding, and the serine/threonine kinases (CIPKs) contain a NAF/FISL motif (indicating the relevant amino acids, N, A, and F) in the C-terminal regulatory domain, which is essential for interaction with CBL proteins.90 Different factors affecting the specificity of interaction between CBL and CIPK proteins must be taken into account. The specificity of protein interactions appears to be dependent on the CIPK kinase domain.91 In addition, CBL phosphorylation by CIPK determines the adequate specificity and activity of CBL–CIPK complexes in regard to their target proteins.92 The CBL–CIPK network would be classified depending on its target: either plasma membrane or tonoplast. While plasmalemma-targeting CBL–CIPK pathways would be essential for plant adaptation to environmental changes, tonoplast-targeting CBL–CIPK pathways would be involved in preventing metal toxicity.86 Although the role of the CBL–CIPK network in the transmission of the Ca signal resulting from biotic stresses is not as clear, its involvement in several abiotic stresses is being widely studied. For instance, CBL10 and CBL4 (SOS3) are described as regulators of salt tolerance mechanisms in Arabidopsis, as their interaction with CIPK24 (SOS2) forms crucial complexes that mediate the exit or sequestration of Na+: while CBL4–CIPK24 regulate the Na+ exit back into soil through SOS1, CBL10– CIPK24 are associated with vacuolar compartmentation mediated by a Na+/H+ antiporter.93–95 The interaction between the type-2C protein phosphatases (PP2Cs) and CIPKs in salt tolerance mechanisms has also been reported.91,96 Moreover, several CBLs–CIPKs participate in ABA-dependent signal transduction pathways,97 as well as in signaling pathways responding to low K.86

Calcium-dependent protein kinases CDPKs are members of the serine/threonine protein kinase family and they can be found in vascular and nonvascular plants, green algae, and certain protozoa.98 CDPK proteins contain a kinase catalytic domain next to the N-terminal domain and a CaM-like Ca2+-binding domain in the carboxyl terminus, which is involved in protein activation after Ca2+ binds to EF-hand domains.98 CDPKs, classified as sensor responders, are involved in the sensing and transmission of changes in cytosolic Ca2+ concentration through phosphorylation events that trigger downstream signaling responses. Till now 34 CDPK

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members and 8 additional CDPK-related kinases have been identified in Arabidopsis.69 CDPKs are present at different locations, such as plasma membrane, cytosol, and nucleus, or also in cellular compartments, such as endoplasmic reticulum or peroxisomes.77 Several studies have revealed the involvement of CDPKs in abiotic and biotic stress responses, as well as in developmental processes; this role sometimes being related to mitogen-activated protein kinases (MAPK) pathways and/or ABA-mediated signaling.77,99 Very recently, it has been reported that CDPK33 and a thiamine thiazole synthase (THI1) could regulate stomatal closure in Arabidopsis.100 Thus, the kinase activity of CDPK33 can participate in the signaling pathway that controls stomatal closure mediated by ABA, in which THI1 can act as a positive regulator in plant drought response.100

CALCIUM USE EFFICIENCY IN PLANTS Nutrient use efficiency (NUE) shows the ability of crops to take up and utilize nutrients for maximum yields. Therefore, the NUE concept involves three major processes in plants: uptake, assimilation, and utilization of nutrients.101,102 Calcium use efficiency (CaUE), which can be defined as shoot or grain yield per unit of Ca accumulated in shoot or grain, varies with crop species, crop tissue, and age. CaUE is higher in cereals than in legumes, and greater on a grain basis than on a shoot basis, regardless of species.4 Genetic variation within and among crops for NUE is well recognized. Genotypic variability, that affects NUE and nutrient uptake, influences some processes and plant mechanisms, including differences in uptake, movement in root, shoot demand, and biomass production.101,103 Intraspecific genetic variability also affects NUE. Variations in the Ca efficiency ratio (milligrams of total plant dry weight per milligrams of Ca in total plant) of cauliflower104 and collard105 cultivars have been reported. In addition, genetic variation in CaUE of tomato strains under low Ca2+ stress has been demonstrated, with efficient strains producing 30% more total plant dry weight than inefficient genotypes.106 In the same way, these authors also proved that CaUE is a highly heritable trait in tomato.106 Consequently, crop productivity and CaUE may be improved by selection of genotypes adaptable to wide range of ecological change, including high tolerance to biotic and abiotic constraints, especially in the current global change context. There is a huge range of external factors (such as climate, soil, biological, management practices, etc.) that affects a plant’s ability to take up and utilize nutrients more effectively. The improvement of CaUE also involves exploring better management practices related to these external factors.4,101,102 Acid soils with low base saturation, high leaching capacity, weathering, and toxic levels of Al, Mn, and Fe usually show Ca deficiency, which can lead to reduced growth and lower NUE.101 In fact, Ca deficiency was a limiting factor for rice production in acid soils.107 In a forestry context, tree growth can be restricted principally by Ca in cation-depleted tropical rain forests.108 Nevertheless, increases in NUE at low nutrient supply rates as a response to the nutrient stress were reported.109 This fact has been illustrated for Ca in tropical forests.21 The application of Ca as an amendment in acid soils is common. Liming, gypsum application, or mixing of both is an effective practice to increase pH, improve Ca content, and control Al toxicity. Moreover, a wide range of industrial wastes (such as sugar foams, biomass ashes, etc.) have been used as liming materials.110 Crusciol et al.111 studied tropical soil and showed that the application of soil acidity amendments and phosphogypsum mixture influences grain yield of upland rice in a positive way.

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Salinity of soil may significantly reduce plant concentration of different nutrients, such as Ca, Mg, and P. It has been reported that high salinity (NaCl) can induce Ca deficiency in leaves of strawberry cultivars.112 Supplementary Ca application into nutrient solution (or soil) ameliorated the negative effects of salinity on plant growth, seed germination, biomass production, and fruit yield.113,114 The application rate of fertilizers or amendments, time of application, and placement play important roles in NUE.115 Defining the Ca optimal application rate to improve CaUE is usually difficult, but necessary. Climate, soil characteristics, species, and cultivars within species are factors that affect this optimal rate. An economic rate for grain yield in annual crops, estimated as 90% of the maximum grain yield, was achieved at about 6 Mg lime ha–1 in soybean.116 Different algorithms have been proposed to obtain optimal rates.117 The severity of Ca-deficiency disorders in storage organs can also significantly increase due to reduced availability of soil water. This is an apparent consequence of Ca supply linkages to the transpiration stream.21 The soil organic matter helps increase water-holding capacity and exchangeable Ca, K, and Mg, and improves soil structure. Lal118 reviewed the best management practices of enhancing the soil organic matter. Another important factor to take into account for the improvement of CaUE is the interaction among nutrients. Evaluating interactions among Ca and other nutrients, especially NH +4 (common fertilizer), Mg2+, and K+, are key issues owing to the inhibitory role on Ca2+ uptake.4,21 Furthermore, in lands that are increasingly rich in N because of a continuous supply of this element (e.g., N fertilization or biological N fixation), Ca depletion may be produced due to simultaneous leaching of both macronutrients, which could result in limitation of growth.119,120 Soil management practices may be crucial for nutrient cycling and NUE improvement of agricultural and forestry systems. Tillage breaks soil aggregates and lowers soil organic matter, nutrients, and microbial activity.121 More macropores and biochannels may be obtained by reduced tillage, impacting water movement, leaching, and loss of nutrients.122 Finally, cover crops reduce erosion, increase water infiltration, and recycle nutrients.122 Regarding biological factors, mycorrhizal weathering of apatite (calcium phosphate) has been documented as an important Ca source in base-poor forest ecosystems.123 Calcium has also showed interaction with different species of Rhizobium bacteria. Positive effects of Ca on Rhizobium growth and nodulation capacity have been described.124,125 Finally, competition among plants for water, nutrients, and sunlight can decrease crop yields and consequently NUE. However, some competitive plant species may alter nutrient cycles differently from native species (root exudates influence soil structure and mobilize or chelate nutrients) and obtain positive interaction effects.126 The invasion of a native winterfat community by annual grass, Bromus tectorum L., increased the availability of Ca and other elements due to its capacity to affect the vertical distribution of nutrients in the soil profile.127

CONCLUSIONS Calcium is an essential nutrient for plant development and crop yield, whose significance is shown in the cell wall structure. In addition, Ca acts as a signal in many cellular processes and in response to biotic and abiotic stresses. Calcium is taken up from the soil by the roots and transported to the shoot through the xylem. Three membrane-located proteins are found in plant cells, which are related to Ca2+

REFERENCES

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movement across membranes, namely, Ca2+-permeable channels, H+/Ca2+ antiporters, and Ca2+ ATPases. Upon a stimulus, Ca2+ channels increase cytosolic Ca2+ concentration to trigger Ca signals, and cytosolic Ca levels are restored by H+/Ca2+ antiporters and Ca2+ ATPases. A great progress has been achieved in the understanding of plant signaling pathways through the use of molecular techniques. The manipulation of Ca signaling pathways is a challenge that allows understanding the mechanisms underlying plant–environment interactions, as well as improving the response of plants to environmental stresses.

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71. Hwang I, Sze H, Harper JF. A calcium-dependent protein kinase can inhibit a calmodulin-stimulated Ca2+ pump (ACA2) located in the endoplasmic reticulum of Arabidopsis. Proc Natl Acad Sci USA 2000;97: 6224–9. 72. Pittman JK, Bonza MC, De Michelis MI. Ca2+ pumps and Ca2+ antiporters in plant development. In: Geisler M, Venema K, editors. Transporters and pumps in plant signaling. Berlin: Springer; 2011. p. 133–61. 73. Limonta M, Romanowsky S, Olivari C, et al. ACA12 is a deregulated isoform of plasma membrane Ca2+ATPase of Arabidopsis thaliana. Plant Mol Biol 2014;84:387–97. 74. Pittman JK. Vacuolar Ca2+ uptake. Cell Calcium 2011;50:139–46. 75. Qudeimat E, Faltusz AMC, Wheeler G, et al. A PIIB-type Ca2+-ATPase is essential for stress adaptation in Physcomitrella patens. Proc Natl Acad Sci USA 2008;105:19555–60. 76. Hashimoto K, Kudla J. Calcium decoding mechanisms in plants. Biochimie 2011;93:2054–9. 77. Batisticˇ O, Kudla J. Analysis of calcium signaling pathways in plants. Biochim Biophys Acta 2012;1820: 1283–93. 78. Kim MC, Chung WS, Yun D-J, et al. Calcium and calmodulin-mediated regulation of gene expression in plants. Mol Plant 2009;2:13–21. 79. Snedden WA, Fromm H. Calmodulin as a versatile calcium signal transducer in plants. New Phytol 2001;151:35–66. 80. Liu H-T, Gao F, Li G-L, et al. The calmodulin-binding protein kinase 3 is part of heat-shock signal transduction in Arabidopsis thaliana. Plant J 2008;55:760–73. 81. Snedden WA, Fromm H. Calmodulin, calmodulin-related proteins and plant responses to the environment. Trends Plant Sci 1998;3:299–304. 82. Zeng H, Xu L, Singh A, et al. Involvement of calmodulin and calmodulin-like proteins in plant responses to abiotic stresses. Front Plant Sci 2015;6:600. 83. DeFalco TA, Marshall CB, Munro K, et al. Multiple calmodulin-binding sites positively and negatively regulate Arabidopsis CYCLIC NUCLEOTIDE-GATED CHANNEL12. Plant Cell 2016;28:1738–51. 84. Geisler M, Frangne N, Gomès E, et al. The ACA4 gene of Arabidopsis encodes a vacuolar membrane calcium pump that improves salt tolerance in yeast. Plant Physiol 2000;124:1814–27. 85. Kudla J, Xu Q, Harter K, et al. Genes for calcineurin B-like proteins in Arabidopsis are differentially regulated by stress signals. Proc Natl Acad Sci USA 1999;96:4718–23. 86. Mao J, Manik SMN, Shi S, et al. Mechanisms and physiological roles of the CBL-CIPK networking system in Arabidopsis thaliana. Genes 2016;7:62. 87. Deng X, Hu W, Wei S, et al. TaCIPK29, a CBL-interacting protein kinase gene from wheat, confers salt stress tolerance in transgenic tobacco. PLoS One 2013;8:e69881. 88. Yu Q, An L, Li W. The CBL−CIPK network mediates different signaling pathways in plants. Plant Cell Rep 2014;33:203–14. 89. Jin X, Sun T, Wang X, et al. Wheat CBL-interacting protein kinase 25 negatively regulates salt tolerance in transgenic wheat. Sci Rep 2016;6:28884. 90. Albrecht V, Ritz O, Linder S, et al. The NAF domain defines a novel protein-protein interaction module conserved in Ca2+-regulated kinases. EMBO J 2001;20:1051–63. 91. Batisticˇ O, Kudla J. Plant calcineurin B-like proteins and their interacting protein kinases. Biochim Biophys Acta 2009;1793:985–92. 92. Hashimoto K, Eckert C, Anschütz U, et al. Phosphorylation of calcineurin B-like (CBL) calcium sensor proteins by their CBL-interacting protein kinases (CIPKs) is required for full activity of CBL-CIPK complexes toward their target proteins. J Biol Chem 2012;287:7956–68. 93. Qiu Q-S, Guo Y, Dietrich MA, et al. Regulation of SOS1, a plasma membrane Na+/H+ exchanger in Arabidopsis thaliana, by SOS2 and SOS3. Proc Natl Acad Sci USA 2002;99:8436–41. 94. Gong D, Guo Y, Schumaker KS, et al. The SOS3 family of calcium sensors and SOS2 family of protein kinases in Arabidopsis. Plant Physiol 2004;134:919–26.

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117. Lukin VV, Epplin FM. Optimal frequency and quantity of agricultural lime applications. Agr Syst 2003;76: 949–67. 118. Lal R. Challenges and opportunities in soil organic matter research. Eur J Soil Sci 2009;60:158–69. 119. Mainwaring DB, Maguire DA, Perakis SS. Three-year growth response of young Douglas-fir to nitrogen, calcium, phosphorus, and blended fertilizers in Oregon and Washington. Forest Ecol Manag 2014;327:178–88. 120. Hynicka JD, Pett-Ridge JC, Perakis SS. Nitrogen enrichment regulates calcium sources in forests. Glob Change Biol 2016;22:4067–79. 121. Plante AF, McGill WB. Soil aggregate dynamics and the retention of organic matter in laboratory-incubated soil with differing simulated tillage frequencies. Soil Till Res 2002;66:79–92. 122. Bronick CJ, Lal R. Soil structure and management: a review. Geoderma 2005;124:3–22. 123. Blum JD, Klaue A, Nezat CA, et al. Mycorrhizal weathering of apatite as an important calcium source in base-poor forest ecosystems. Nature 2002;417:729–31. 124. Reeve WG, Tiwari RP, Dilworth MJ, et al. Calcium affects the growth and survival of Rhizobium meliloti. Soil Biol Biochem 1993;25:581–6. 125. Watkin ELJ, O’Hara GW, Glenn AR. Calcium and acid stress interact to affect the growth of Rhizobium leguminosarum bv. trifolii. Soil Biol Biochem 1997;29:1427–32. 126. Weidenhamer JD, Callaway RM. Direct and indirect effects of invasive plants on soil chemistry and ecosystem function. J Chem Ecol 2010;36:59–69. 127. Blank RR. Biogeochemistry of plant invasion: a case study with downy brome (Bromus tectorum). Invasive Plant Sci Manag 2008;1:226–38.

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10

THE ROLE OF CALCIUM IN PLANT SIGNAL TRANSDUCTION UNDER MACRONUTRIENT DEFICIENCY STRESS

Ashish Sharma*, Deepti Shankhdhar**, Shailesh C. Shankhdhar** *DAV University, Jalandhar, Punjab, India **G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

INTRODUCTION Calcium (Ca) is one of the most important nutrients required for normal plant growth and development. The role of Ca as a secondary messenger is well known in both plant and animals with the concentration of Ca in plant cells being delicately balanced between the cytosol, cellular compartments, such as vacuoles, endoplasmic reticulum, and the cell wall.1 Changes in intracellular Ca levels are involved in the transduction of many intracellular signals used to regulate the developmental and environmental responses of plants. Ca is never actively excreted out of plant cells and its concentration is maintained in the millimolar range in the cell cytoplasm.1,2 Calcium ion (Ca2+)-mediated signaling is very important and Ca forms a convergence point for many signaling pathways in plants, as well as animal cells.2 Stress-associated environmental signals trigger a series of signaling events in plant cells that start with stress perception at membrane level and may then involve the expression of nuclear genes and ends with generation of cellular response. In general, a signal transduction pathway includes: 1. activation of membrane receptors by external stimuli, 2. generation of second messengers, 3. a cascade of events that may regulate a specific set of genes, and 4. generation of cellular and phenotypic responses. The overall response of the plant to the external stimulus often involves the coordinated expression of many genes and cross-talk between many signaling pathways. Successful signal transduction requires the precise coordination of all signaling molecules in both space and time. In plants molecules like Ca2+, inositol-3-phosphate (IP3), and cyclic GMP (cGMP) have been found to function as second messengers.3 However, Ca2+ has been shown to participate in a more diverse array of cellular processes than any other second messenger. The Ca-mediated response can be as extreme as growth inhibition and/or cell death.4 In response to changes in the cytosolic Ca2+ concentration, some genes are upregulated and some genes are downregulated. It has been revealed by transcriptome analysis of Arabidopsis root cells that Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00010-7 Copyright © 2017 Elsevier Inc. All rights reserved.

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there are approximately 230 Ca2+ responsive genes, out of which 162 are upregulated and 68 are downregulated, in which many stress responsive genes are included. Under abiotic stress, plant responses, such as regulation of potassium (K) channel, sucrose signaling resulting in fructan synthesis, cell cycle progression are also regulated by the Ca2+ signaling.4,5 Hence, considering the importance of Ca2+ in various signaling cascades in plants, this review will focus on the Ca2+ requirements of plant, Ca2+ deficiency, Ca2+ transporters and efflux pumps, Ca2+/H+ antiporters, Ca2+ signatures, Ca2+ memory, Ca2+ sensors and transducer proteins, along with the role played by Ca2+ ions in signaling under macronutrient deficiency.

CALCIUM IN PLANTS Ca is a macronutrient required by plants at approximately 5000 mg kg−1 dry matter (0.5%) and is essential for the growth and development of plants. It is very important for the development of meristematic regions of roots and shoots. It regulates anaphasic movement of chromosomes by regulating the assembly and disassembly of microtubules.2 It is also required for the maintenance and integrity of the cell wall, as Ca is needed for the formation of Ca pectates that act as the main cementing material to hold plant cells together. Ca2+ is important to counteract the cytotoxic effect of other ions, like it can also reduce the toxic effects of NaCl if supplied externally (Fig. 10.1). Along with the above and in addition to its role as second messenger, Ca+ promote pollen tube growth and elongation in plants.6,7 Most of the Ca present in plants is taken up by roots and transported to the shoots by xylem.1 Both symplastic and apoplastic routes have been demonstrated for the movement of Ca2+ in roots. 200 nM is the threshold concentration of free cytosolic Ca2+ ions that is maintained by plant cells but the concentration of active Ca in the cytosol is much higher, as Ca often exists bound to proteins.8 Ca content of various plants ranges between 0.1% and 5% dry weight of different plants and tissues. The concentration of Ca in the soil also varies widely from very low in acid lateritic (rocky soils rich in Fe and Al and having a pH of 4.5–6.5) soil to very high in chalky (limestone rich soil having pH above7.5) soils. Plants can be classified as calcifuges—growing on acidic soil with low pH, and calcioles—growing on Ca rich soils. The ability of the plants to extract Ca from the soil also varies differently from complex soil environment.2,8 The roots have to compete with plethora of soil microbiota, which also extracts Ca from the soil for their normal growth and development. However, plants do extract Ca from soils in high concentrations. Apart from soil and tissue variations, monocots (maximum concentration reported for Ca is 1.22% on dry weight basis) require less Ca as compared to the dicots (maximum concentration reported for Ca is upto 3.21% on dry weight basis).9 Ca channels present in the plasma membranes are the pathways for entry of the Ca2+ in plant roots, but there exist a competition between cations in the soils for occupying the sites on root surface for uptake of cations. Ca2+ ions are preferred over ions, such as aluminum and sodium, which have toxic effects in the plants, but the presence of K and magnesium (Mg) ions in the rhizosphere reduce the uptake of Ca.1 Therefore, it is safe to say that Ca uptake and cytosolic concentrations are linked to the levels of other cations present in the soil. Soils usually have sufficient Ca except for highly acidic soils. Ca deficiency primarily occurs when the rate of transpiration exceeds the rate of water absorption of the roots, which ultimately affect rate of flow in xylem. Ca deficiency results in the reduction in root growth, malformed leaves, loss of membrane integrity, bitter-pit in apple, blossom end rot in tomato, and tip burn in lettuce. Deficiency symptoms develop in a basipetal manner in most cases.8

 Introduction

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FIGURE 10.1  The Role and Requirement of Calcium in Different Plant Parts

MEMBRANE CALCIUM TRANSPORTERS Plant cells maintain free Ca concentrations in the range between 30 and 400 nM by actively transporting the Ca2+ ions from the cytosol. After every signaling event, active efflux takes place to maintain the Ca concentration in the cytosol. During efflux Ca2+ ions are either moved out into the apoplast or sequestered into the endoplasmic reticulum or vacuole.10 Ca2+ ATPases and H+/Ca2+ antiporters are the key proteins involved.11 Ca removal from cytosol is required: 1. to maintain low cytosolic Ca in the resting cell 2. to avoid unnecessary signaling events, this may result from increased Ca concentration 3. to maintain the cytosolic Ca oscillations between intra and extracellular Ca stores 4. to maintain high levels of Ca in ER for normal functioning of secretory system 5. to prevent mineral toxicity caused by divalent cations, such as Ni2+, Zn2+, Mn2+ in the cytosol Evidence has been obtained showing that Ca2+ ATPases are involved in the maintenance of cytosolic Ca homeostasis in resting cells, whereas H+/Ca2+ antiporters have been reported to be involved in the

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removal of Ca2+ from the cytosol after signaling events, thereby maintaining cytosolic Ca levels.11,12 The two major types of Ca-ATPases found in plants are: 1. the P-type ATPase II A family 2. the P-type ATPase II B family The individual isoforms of Ca2+ ATPases are distinct and are involved in different cellular processes. In addition to the ATPases, low affinity H+/Ca2+ antiporters have been shown to be present in the plasma membrane.10 These transporters have a low affinity for Ca and transport, one Ca2+ ion for three H+ ions. The antiporters use the proton gradients generated by other pumps, such as V-type ATPases to sequester Ca2+ ions into the vacuoles, which makes a very significant contribution to the regulation of cytosolic Ca concentrations. Both these pumps are the Ca2+ efflux transporters, in contrast Ca influx to the cytosol is mediated by Ca channels present in the plasma membrane. The principle role of these channels is to passively import the Ca2+ during signaling. These channels have been found in all plants and have been classified on the basis of their voltage dependence or potential change required for opening.11–13

CALCIUM SIGNATURES AND MEMORY Various shifts in the plants microenvironment lead to changes in the cytosolic Ca concentration in the cells. This increase and/or decrease in the cytosolic Ca concentration, termed as “Ca signatures” in response to environmental and developmental cues, is critical for generation of a physiological response.14 One of the major reasons for the increase in the cytosolic Ca levels is the influx of Ca2+ into the cytosol either from the apoplast through the plasma membrane or from intracellular sources. Different mechanisms have evolved for regulation of cytosolic Ca2+ concentration in plants,15 which enable the cells to generate complex Ca2+ signatures that can relay information relating to the nature and strength of stimulus in a time-space specific manner. These mechanisms include Ca2+ influx channels in plasma membrane and endomembranes for Ca release into the cytosol, contributing to stimulus specific Ca signatures. A majority of Ca2+ influx channels in the plants are permeable to cations instead of being Ca2+ selective, which include three different types of plasma membrane Ca2+ channels, viz., hyperpolarization-activated Ca2+ channels (HACC), depolarization-activated Ca2+ channels (DACC), and mechanosensitive Ca2+ channels (MCC).16 MCCs are considered to be good candidates for shaping Ca2+ signals in cells, as they have been found in different cell types, including guard cells, mesophyll cells, pollen grains, and pollen tube protoplasts. In different studies, MCCs have been shown to be important components of signaling pathways in response to the mechanical stimuli, for instance, pollen tube germination and elongation depends on influx of Ca2+ through MCCs.17 One of the first responses of plant cells against the external disturbances is the depolarization of plasma membrane suggesting a plausible role of DACCs in changing cytosolic Ca2+ signatures in response to stress. Activity of DACCs has been reported in many tissues in maize and Arabidopsis, however, their activity has mostly been demonstrated under the culture conditions.15 On the other hand, HACCs has been proven to be a key component in generating Ca2+ signatures in guard cells and root hair cells in response to stimuli, like abscisic acid and reactive oxygen species.18 The identity of guard cell HACCs was determined in mutant studies to be ABC transporters,19 which are activated/deactivated in response to ABA. Ca channels have also been studied in the endomembrane systems, but assigning specific activities to the channels in relation to generation of specific Ca2+ signals in cytosol is difficult due to inability to

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study the endomembranes in intact cells. Four putative Ca2+ permeable channels have been studied in the endomembranes that may contribute to the generation of Ca2+ signatures, these are inositol-1,4,5triphosphate gated channels, vacuolar voltage gated channels, slow activating vacuolar channels, and cADPR-gated channels. All these channels have been reported to release Ca2+ into the cytosol from vacuoles under stress conditions, thereby regulating Ca2+ signatures in cytosol.15 Such changes generate a cytosolic Ca “wave” by successive opening and closing of the membrane Ca channels, the traversing of which generate many soluble second messengers, like IP3 that diffuse through the cytoplasm to activate other Ca channels.20,21 Knight et al.22 introduced the term Ca memory, which suggest that change in the cytosolic Ca concentration will affect the change in the subsequent Ca signature generated in the cytosol. The cytosolic Ca2+ changes are universal signals in biological systems and memory of Ca2+ signatures generated in the cytosol is important over long range signaling in the plants. For example, hormonal transport in the vascular system triggers a change in Ca2+ concentration at distant sites, such as ABA produced in the water-stressed roots has been shown to change Ca2+ signatures in the guard cells rendering them closed. In another instance it was shown that Ca2+ being transported in the transpiration stream generates a Ca2+-dependent response in the plastids. Exactly how these responses work has been a point of debate for quite some time. These changes can be accredited to the memory of Ca2+ signatures retained by the target cells. It has been shown that modified response of the cytosolic Ca concentration after repeated stimulation by different stimuli is an important part of cellular memory, where cells retain the previous information. This “memory” is significant and helps the cells to respond better to a particular stress without disturbing the delicate balance of Ca levels while maintaining cellular Ca homeostasis.23

CALCIUM-BINDING PROTEINS A number of different Ca sensors are present in the cytosol that can sense the changes in the cytosolic Ca concentrations.24 These sensors are small Ca-binding proteins, which show Ca dependent conformation changes. These proteins provide signal specificity owing to their precise identification of perturbations in cytosolic Ca concentrations.24 Ca-binding proteins must possess the following characters: 1. The Ca receptor proteins must possess free Ca binding sites in the resting phase of the proteins. 2. The proteins must be selective and show preference for Ca in presence of other divalent cations. 3. After reversibly binding to the Ca, the protein must undergo either a conformational change or activity change. 4. The binding and unbinding should be very fast in the cytosol. 5. These proteins must possess a helix-loop-helix domain called an EF hand or elongation factor hand domain for binding of the Ca ions. The major families of Ca sensors found in plants include the following: 1. Calmodulin (CaM): It is a small acidic protein found in the apoplast, cytosol, ER, and nucleus of the plant cells. It is highly conserved and contains two globular domains having two EF hands each. It has been found to be involved in signaling of many physiological processes, like light, gravity, mechanical stress, phytohormones, pathogens, osmotic stress, heat shock, and chilling. CaM has been demonstrated to be a regulatory protein, which can activate and/or deactivate many cellular proteins when Ca is bound. CaM binds to four Ca2+ and has been found to modify the gene expression also. Along with CaM, plants also possess CaM-like proteins, which differ from

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Table 10.1  Different Calcium Responsive/Binding Proteins Found in the Plant Cells and Their Activities Proteins

Activities

CaM

It is a common multifunctional intermediate calcium binding protein expressed in all eukaryotic cells. It is an intracellular target for calcium, which when bound results in activation of CaM.

CaBP22

22 kD calcium binding protein present exclusively in plant cell cytoplasm and is involved in calcium-mediated signaling events.

AtCP1

It is a protein expressed in leaves, hypocotyls, xylem parenchyma, and root cells. These proteins bind calcium and localize in cytosol and have been proposed to participate in hyperosmotic salinity stress.

TCH3

This protein contains six calcium binding sites and has been localized in the growing regions of roots, root/shoot junction, trichomes, branch points of shoots, and flowers. It functions in a calcium-modulated manner in generating changes in cells and/or tissues that result in greater strength or flexibility.

TCH2

It has almost 40% similarity to CaM. It is a calcium binding protein found in all major organs of plants such as leaves, roots, flowers, xylem, phloem, etc. Its role has been very well elucidated in responses of the plants to touch, darkness, heat, cold, abscisic acid, and indole-3-acetic acid.

ABI1

Identified from Arabidopsis, it encodes a member of 2C class of protein ser/thr phosphatase and is involved in regulating ABA response in seeds and vegetative tissue.

ABI2

It is also a homologue of ABI1 and also involved in ABA sensitivity in seeds and vegetative tissues.

NADPH oxidase It is present attached to the plasma membrane and has been shown to be involved in calciummediated ROS generation mainly in plant defense response. CaM, Calmodulin; ROS, reactive oxygen species.

CaM in the fact that they do not have fixed number of EF hands. Some examples of these proteins include, CaBP-22, AtCP1, TCH2, TCH3, ABI 1, ABI 2, and NADPH oxidases (Table 10.1). They find important roles in developmental, environmental, and pathological responses of the cell.25,26 2. CDPKs: CDPKs or Ca-dependent protein kinases are found in all plants (Table 10.2). As many as 34 genes have been identified from the Arabidopsis genome, which encode for CDPKs. These enzymes contain four EF hands, and when Ca binds, the serine/threonine kinase domains of these enzymes are exposed. These enzymes have two distinct domains, N-terminal that has the kinase activity and an autoinhibitory C-terminal domain. Binding of Ca to the CDPKs changes their conformation, as a result the autoinhibitory domain releases its effect and the enzyme gets activated. CDPKs have been shown to be involved in cell-cycle regulation, phytohormonemediated signaling, development of pollens, light-mediated gene expression, gravitropism, thigmotropism, cold acclimation, salinity tolerance, drought tolerance, and pathogen response. Another class of proteins showing the CDPK like activity is the CDPK-related proteins kinases or CRK for short. These proteins have poorly developed EF hands that sometimes may not be able to bind Ca. There are atleast seven different types of CRKs identified from different plant sources.26,27 3. CCaMKs: Several Ca-calmodulin dependent protein kinases have also been identified from the plant cells. These proteins have been identified from lily and other plant species but their exact role in the plants metabolism and/or development has yet to be demonstrated.28

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Table 10.2  Different Calcium-Dependent Protein Kinases (CDPKs) Isolated From Different Plant Sources CDPKs

Plant Sources

Functions

AtCPK 24

Arabidopsis

Involved in flower and pollen development

AtCPK 17 and 34

Arabidopsis

Found to be involved in pollen-tube elongation

PiCPK 2

Petunia

Involved in pollen-tube extension

PiCPK 1

Petunia

Involved in pollen-tube growth

OsCPK 21, 22, 29

Rice

They have been abundantly found in panicle, seed, and stamen development

OsCPK 21

Rice

Its role has also been demonstrated in cold stress and desiccation tolerance

OsCPK 2

Rice

Its role has been demonstrated in the development of seed

AtCPK 10,13, 30

Arabidopsis

Has been found involved in the response generated to the infection by Erysiphae sp.

OsCPK 9

Rice

Involved in the plant response to Magnoporthe infection.

TaCPK 19, 3, 15

Triticum

Involved in response to the Blumeria graminis infection

OsCPK 7, 13

Rice

Involved in response to the cold stress

OsCPK 13

Rice

Involved in cold tolerance

TaCPK 1, 2

Triticum

Involved in response to the cold stress

AtCPK 5, 6, 26

Arabidopsis

Involved in general signaling in the plant cell cytosol

AtCPK 1, 2

Arabidopsis

Involved in the generation of resistance to Fusarium oxysporum

CanCDPK 3

Capsicum

It has been reported to be upregulated by Xanthomonas axonopodis infection

PaCPK 1

Phalaenopsis amabilis It has been reported to be upregulated by Erwinia chrysanthemi infection

Modified from:Valmonte GR, Arthur K, Higgins CM, MacDiasmid RM, et al. Calcium-dependent protein kinases in plants: evolution, expression and function. Plant Cell Physiol 2014;55:551–69.

4. CBLs: Calcinurin B-like proteins are a class of Ca sensors identified from Arabidopsis, which are mostly involved in maintaining the ion homeostasis in the plants under salt stress. Presently more than 10 CBL and approximately 30 CBL-interacting protein kinases (CIPK) have been identified from different higher plants, but except Arabidopsis their role in other plants still needs to be demonstrated. Both these enzymes have shown the property of autophosphorylation and phosphorylating activities and their primary function has been demonstrated in the stress tolerance in higher plants.29,30 5. There are several proteins that bind Ca but do not have EF hands. These proteins include, phospholipase D (PLD), involved in the cleavage of membrane phospholipids; phospholipase C (PLC), involved in the generation of signaling molecules, like IP3 diacylglycerol (DAG); annexins a proteins family found in both plants and animals which bind to phospholipids in a Ca-dependent manner and mainly involved in the secretary processes; calreticulin, a Ca sequestering protein in the endoplasmic reticulum and PCP (pistil expressed Ca binding protein) a protein expressed in anthers and pistils and involved in pollen–pistil interaction.30–34

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ROLE OF CALCIUM IN MACRONUTRIENT DEFICIENCY Macronutrients are the mineral elements required by the plants in high concentrations. Some of the most important macronutrients include elements, like nitrogen (N), phosphorus (P), K, sulfur (S), Ca, and Mg. In the next part of this review, we shall focus on the role played by Ca in mitigating the impacts of macronutrient deficiency. Investigating elevation in cytosolic Ca concentration in response to nutrient deprivation or resupply is technically challenging, particularly if using aequorin (a Ca-activated photoprotein). Aequorin (AEQ) is a photoprotein that is used extensively for visualizing Ca2+ inside the cytosol. AEQ is composed of an apoprotein (apoaequorin), having an approximate molecular weight of 22 kDa and coelentrazine acting as a prosthetic group, which is a luciferin molecule. The protein contains three Ca-binding sites that are EF-hands. The binding of Ca2+ to AEQ molecule changes its conformation and exposes the oxygenase site that converts coelenterazine into coelenteramide. When the excited coelenteramide relax to its ground state, blue light is emitted35 that can easily be measured and correlated to Ca2+ concentration. A major disadvantage of using AEQ is the low magnitude of the emitted signal. Although the amount of signal emitted by the cell population is quite adequate to measure Ca2+ concentration, the signal from single cell is very low, which is important so as to overcome the background noise. Another limitation of using AEQ is overestimation of cells real response, especially when cell suspensions are used instead of whole tissues.36 Nevertheless, it is now clear that nutrient level can induce changes in the cytosolic Ca levels and that downstream Ca sensors regulate appropriate responses.37,38 To date, K, nitrate, and Boron have been studied but B being a micronutrient, the aegis of Ca signaling in B deficiency shall not be included in this chapter.

POTASSIUM K is fundamental to normal plant metabolic and developmental processes, hence K is designated as essential macronutrient for all plants. The K content of a plant can reach up to 10% of dry weight, making it the most abundant cation in plant cells. The cytoplasmic concentration of K+ ranges between 40 and 200 mM, with an average of 100 mM, and is relatively stable which is a requirement for the maintenance of cytoplasmic enzyme functions. K ions are taken up by the plants against a concentration gradient, as the concentration of K is comparatively low, usually in the micromolar range in rhizospheric zone. For the uptake of K+ many different channels and transporters have been identified from the plasma membranes of root hairs in higher plants. For example, a total number of 71 channels and transporters specific for K have been identified in Arabidopsis only, which have mainly been divided into six families that include three channel forming gene families (Shaker, TPK, and Kir-like families) and three transporter protein families (KUP/HAK/KT, HKT, and CPA families). Shaker channels have been identified as the most important K uptake channels for plants to acquire K from the soils, out of which AKT1, an inward rectifying K+ channel, is the most important and has mostly been found in the root hair cells of Arabidopsis plants.39–41 Most of the K in plants is absorbed by the root hair cells; hence, roots are the primary organs to experience the deficiency symptoms of K ions. One of the most important feature of K deficiency and the first experienced change by the plants is hyperpolarization of membrane potential in root cells, which can appear within minutes of K concentration change. It is different form of membrane depolarization caused by increase in K concentration as decrease in K concentration is accompanied by extrusion of protons and acidification of extracellular matrix. This hyperpolarization of membrane is the result of

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presence of an H+-ATPase in the plasma membrane that selectively transports H+ from cytosol to the apoplast.42–44 Signaling involving Ca ions can be considered as the most important signaling system in the higher plants. It is important in plant response to the variations in the external environment (abiotic stresses), including nutritional deficiency. Membrane Ca channels, cytosolic Ca sensors, and reactive oxygen species (ROS) are the major components of pathways for generation and transduction of Ca signals in the cytosoilc environment. It has been reported that ROS and Ca2+ sensors are very important in sensing and signaling under K deficiency conditions. The model shown in Fig. 10.1 shows the generation and transduction of signals in K deficiency condition in plant cells. It also depicts involvement of Ca and ROS signals, ion channels, and transporters along with their regulatory proteins.45,46 Hyperpolarization of root hair plasma membrane results in the activation of Ca channels present in the plasma membrane. As a result, reduced concentration of available K ions in the root’s external environment can induce a spike in the concentration of Ca in cytoplasm. ROS has been demonstrated as an important molecule in many plant signaling pathways and it has also been reported that ROS generation in plants lead to generation of Ca signals in most of the instances, involving ROS molecules. Activation of ROS-mediated pathway in response to K deprivation also results in the opening of still more Ca channels present in the plasma membrane of the roots. According to many reports in the past decade, a strong induction in the ROS signatures has been observed and demonstrated in many plant species. It has been observed that both generation of ROS signature and increase in the cytosolic Ca concentration show an intercatalytic effect, where generation of ROS signatures results in opening of membrane Ca channels and increase in the cytosolic Ca concentration leads to induction of ROS molecules. The overall result of this process is increase in the cytosolic Ca concentration. These changes in the Ca concentration in the cytosol acts as signals for Ca sensors that then carry the information to other organelles or proteins or nucleic acid in the cell. The various Ca sensors that are present in the cytosol and that can participate in the Ca mediated signaling have been described above. Whether all these sensors respond to K+ deprivation or some other molecules are present that sense changes in the Ca concentration in response to K deprivation still remains to be established.46–48 Ca signals are generated in the cytosol in response to the reduction in extracellular K concentration, which is sensed by the plants as low K stress and these signals are further decoded by the cytosolic Ca sensors. These changes are further transduced and K channels and transporters are activated/deactivated at transcriptional and/or posttranscriptional level (Fig. 10.2). As a result, plants adapt to the decreased K concentration in the environment by maintaining the K homeostasis in the cells.49

Transcriptional regulation

Several K transporters in Arabidopsis are transcriptionally induced by K+-starvation, such as AtHAK5, AtKEA5, AtKUP3, AtCHX13, and AtCHX17. Among these transporters, HAK5, AtCHX13, and AtCHX17 were confirmed to be involved in K+ acquisition and homeostasis in plants under the K+deficient conditions. The generation of ROS is necessary for the induction and transcriptional upregulation of HAK5 (Fig. 10.2). The K-starvation-induced transcriptions of K transporters were also reported in other species, such as LeHAK5 in tomato, HvHAK1 in barley, CaHAK1 in pepper, and OsHAK1 from rice. These K transporters may be considered as the initial response genes that may function in enhancing K uptake and redistribution during the early stage of K deficiency. Unlike K transporters, few K+ channels show change at a transcriptional level in response to reduced K concentration, even if some K+ channels respond to other environmental and hormonal stimuli. Transcription

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FIGURE 10.2  Schematic Model of Plant Sensing and Signaling in Response to Potassium Deficiency Adapted from: Wang Y, Wu WH. Plant sensing and signaling in response to K+-deficiency. Mol Plant 2010;3: 280–87.

of AtKC1 in Arabidopsis roots is induced under K deficient conditions. Transcriptions of LeKC1 from tomato and TaAKT1 from wheat were also found to be induced by K deprivation. In addition to these, K transporters and channels, many other genes related to transcription, protein phosphorylation, ROS metabolism, Ca signal generation and transduction, ethylene synthesis, as well as jasmonic acid synthesis are also upregulated in response to K starvation. Further functional characterization of these K deprivation responsive genes may result in identification of some new signaling components in plant response to K deficiency.50–55

Protein modification Regulation of K channel and transporter activities by posttranslational modulations may play crucial roles in plant signal transduction in response to K deprivation. More and more evidences show that the phosphorylation and dephosphorylation of K channel proteins controlled, respectively, by kinases and phosphatases may be the important mechanisms for plant responses to K deficiency.

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A calcinurin-B-like protein (CBL)-mediated Ca signaling pathway regulating K uptake under K deficiency conditions was identified in Arabidopsis roots. The plasma membrane located Ca sensor CBL1 (and/or CBL9) interacts with a cytoplasm-located Ser/Thr kinase CIPK23 and recruits it to the plasma membrane, where CIPK23 activates AKT1-mediated K uptake via phosphorylation. The CBL1 and CBL9, as the sensors to perceive Ca signal induced by K deprivation, may be the main components to sense and transduce K deprivation signals Fig. 10.2. On the other hand, the phosphatase AIP1 could deactivate AKT1 channel activities through dephosphorylation to cease the K deficiency induced signaling.29,55,56 Another K deficiency signaling pathway has been identified in Arabidopsis, in which the tonoplastlocated CBL proteins may be involved. The vacuole is an important pool for many mineral elements in plants including K that regulates the cytoplasmic K homeostasis by accumulating K or releasing it into cytoplasm. It is speculated that CIPK9 may interact with a tonoplast-located CBL (CBLx), thereby the CBLx–CIPK9 complex activates tonoplast-located K transporters (such as TPK1 or others) through phosphorylation and facilitates the K+ release from vacuole under K deprivation conditions (Fig. 10.2).29,57–59

NITRATE Nitrate (NO −3 ) is the most important form of N for agriculture and its deficiency triggers significant transcriptional and developmental responses. The effect of NO −3 withdrawal on cytosolic Ca concentration has yet to be reported, but recently it has been shown that NO −3-starved Arabidopsis roots responded to NO −3 resupply with a rapid, monophasic transient increase in cytosolic Ca concentration that was sensitive to lanthanides and PLC inhibition. Lanthanum also blocked NO −3-induced inositol triphosphate (InsP3) production, suggesting that Ca influx across the plasma membrane activated a PLC. The cytosolic Ca concentration and InsP3 increases were entirely dependent on the PM nitrate influx transporter NRT1.1 (nitrate transporter1.1). By using the nrt1.1 mutant and pharmacological blockers, NO −3-induced gene transcription was also found to lie downstream of NRT1.1, and cytosolic Ca concentration elevation from PM influx and InsP3-gated store release. Ca is also key to the regulation of NO −3 uptake capacity as CIPK23, which is activated by CBL9 and CBL1, and dephosphorylated by ABI2 (a member of the PP2C protein phosphatase family), phosphorylates NRT1.1 under low-NO −3 condition, thus converting it from a low- to high-affinity transporter. In contrast, CIPK8 positively regulates the lowaffinity phase of the NO −3 primary response, which includes transcriptional regulation, but its regulation of primary root elongation is concentration independent in Arabidopsis. CBL7, which is upregulated under NO −3 deprivation, positively regulates the NO −3-dependent induction of NTR2.4 and NTR2.5 gene expression. Given the lack of a cytosolic Ca reporter line available in crops up until recently for rice, little is known about nitrate deficiency-induced cytosolic Ca signaling but CaM protein abundance of wheat roots declines under NO −3 deficiency, suggesting a remodeling of signaling systems.60–62

MAGNESIUM Mg was considered essential nutrient by as early as 1925.63 In subsequent years it was found to be essential for macromolecular stability,64,65 maintenance of cell walls and cell membranes,66 maintenance of activity of enzymes, such as H+-ATPase, kinases, and polymerases,66–68 and mediation of generation and degradation of ROS. One of the most important roles of Mg is in the maintenance of cell turgor

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by acting as osmotically active ion along with K,69 and it has also been reported to play an important role in maintaining the cation–anion balance. Most of the Mg found in plants is present in leaves, of which almost 75% is involved in protein synthesis and rest is associated with the chlorophyll present in the leaf chloroplasts.70 Along with chlorophyll, leaf Mg is also involved with a number of enzymes acting as cofactor involved in carbon fixation reactions and metabolism.7 Along with these, substantial amounts of Mg is also found in the actively dividing parts of the plants, where it acts as a cofactor of DNA polymerase enzyme. Major symptoms visible of magnesium deficiency (MGD) include reduced growth of roots, shoots, and appearance of necrotic spots on leaves owing mainly to the reduction in chlorophyll concentration and reduced carbon metabolism.68,71,72 However, a more in depth analysis of Mg-deprived plants showed a plethora of genes involved in the processes affected by MGD in the plants. A comprehensive view of different processes affected by Mg deprivation in plants has been presented in Fig. 10.3. There is little available data pertaining to the investigations on signal transduction in response to MGD in plants. However, a change in the expression pattern of many genes has been reported, which include genes responsible for ROS detoxification and ABA response among others.73 It was proposed that cytosolic Ca2+ and ROS are the major players in the Mg deprivation as it has been shown that ROS generated in the leaves in response to Mg deprivation result in the degreening of leaves ultimately resulting in the appearance of necrotic spots on the leaves. In addition, RUBISCO and enzymes for starch synthesis has been shown to be sensitive to the increased ROS concentration under high-light and low-temperature conditions.74,75 The most prominent role of Ca in MGD has been demonstrated in

FIGURE 10.3  Different Processes Affected in the Plants by Magnesium Deficiency Adapted from: Guo W, Nazim H, Liang Z, Yang D, et al. Magnesium deficiency in plants: an urgent problem. Crop J 2016; 4:83–91.

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the reduction in the development of roots and root hairs, where the data is still insufficient to explain the precise role of Ca that whether it is involved in Mg homeostasis or control the MGD response in some other manner.

CONCLUSIONS AND FUTURE PERSPECTIVES It is clear from the documentation provided in this chapter and literature available on Ca, that Ca plays a fundamental role in mitigating and maintaining ion homeostasis under nutritional stress. It is also involved in up- or downregulating many transporter genes required for the uptake of various nutrients in the plasma membrane. The Ca signature generated in the cytoplasm of plant cells play an important role in the all the processes described above. Besides Ca signatures, a variety of Ca binding proteins and protein kinases/phosphatases are present in the cytoplasm, which can either activate or deactivate the membrane transporters and also cross-talk with other signaling events in the cell cytoplasm. A large amount of data is available on Ca and roles played by Ca in the signaling events in both plants as well as animals. Its role as a second messenger has been well documented in the literature. However, the involvement of Ca in nutritional stress has received less attention in the scientific research, which is clear as literature is only available on K, NO −3 and B. Hence, in future more attention is needed on investigating the role of Ca in nutritional stress in plants. Deficiency created by nutrients, like P, Mg, Zn, and Fe is important and devastating. Hence, these deficiency constraints hold immense potential for further research.

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13. Bonza MC, Luoni L, Olivari C, DeMichelis MI, et al. Plant type 2B Ca2+-ATPases: the diversity of isoforms of the model plant Arabidopsis thaliana. Adv Biochem Health Dis 2016;14:227–41. 14. Mahajan S, Tuteja N. Cold, salinity and drought stresses: an overview. Arch Biochem Biophys 2005;444:139–58. 15. McAinsh MR, Schroeder JI. Crosstalk in Ca2+ signaling pathways. In: Yoshioka K, Shinozaki K, editors. Signal cross talk in plant stress responses. Ames, Iowa, USA: Blackwell; 2009. 16. Demidchik V, Maathuis FJM. Physiological roles of nonselective cation channels in plants: from salt stress to signalling and development. New Phytol 2007;175:387–404. 17. McAnish MR, Pittman JK. Shaping the calcium signature. New Phytol 2009;181:275–94. 18. Bright J, Desikan R, Hancock JT, Weir IS, Neill SJ. ABA-induced NO generation and stomatal closure in Arabidopsis are dependent on H2O2 synthesis. Plant J 2006;45:113–22. 19. Suh SJ, Wang YF, Frelet A, Leonhardt N, Klein M, Forestier C, et al. The ATP binding cassette transporter AtMRP5 modulates anion and calcium channel activities in Arabidopsis guard cells. J Biol Chem 2007;282: 1916–24. 20. Kaplan B, Davydov O, Knight H, Galon Y, Knight MR, Fluhr R, et al. Rapid transcriptome changes induced by cytosolic Ca2+ transients reveal ABRE-related sequences as Ca2+-responsive cis elements in Arabidopsis. Plant Cell 2006;18:2733–48. 21. Knight MR, Read ND, Campbell AK, Trewavas AJ, et al. Imaging calcium dynamics in living plants using semisynthetic recombinant aequorins. J Cell Biol 1993;12:83–90. 22. Knight H, Trewavas AJ, Knight MR, et al. Cold calcium signaling in Arabidopsis involves two cellular pools and a change in calcium signature after acclimation. Plant Cell 1996;8:489–503. 23. Gilroy S, Suzuki N, Miller G, Choi WG, Toyota M, Devireddy AR, et al. A tidal wave of signals: Ca and ROS at the forefront of rapid systemic signaling. Trends Plant Sci 2014;19:623–30. 24. Cheng SH, Willmann MR, Chen HC, Sheen J, et al. Calcium signaling through protein kinases. The Arabidopsis calcium-dependent protein kinase gene family. Plant Physiol 2002;129:469–85. 25. Bouché N, Scharlat A, Snedden W, Bouchez D, Fromm H, et al. A novel family of calmodulin-binding transcription activators in multicellular organisms. J Biol Chem 2002;277:21851–61. 26. Harper JF, Huang JF, Lloyd SJ, et al. Genetic identification of an auto inhibitor in CDPK, a protein kinase with a calmodulin like domain. Biochemistry 1994;33:7278–87. 27. Harmon AC, Gribskov M, Harper JF, et al. CDPKs: a kinase for every Ca2+ signal? Trends Plant Sci 2000;5:154–9. 28. Hwang I, Sze H, Harper JF, et al. A calcium-dependent protein kinase can inhibit a calmodulin-stimulated Ca2+ pump (ACA2) located in the endoplasmic reticulum of Arabidopsis. Proc Nat Acad Sci USA 2000;97:6224–9. 29. Drerup MM, Schluecking K, Hashimoto K, Manishankar P, Steinhorst L, Kuchitsu K, et al. The calcineurin B-Like calcium sensors CBL1 and CBL9 together with their interacting protein kinase CIPK26 regulate the Arabidopsis NADPH oxidase RBOHF. Mol Plant 2013;6:559–69. 30. Harper JF. Dissecting calcium oscillators in plant cells. Trends Plant Sci 2001;6:395–7. 31. Lu YT, Hidaka H, Feldman LJ, et al. Characterization of a calcium/calmodulin protein kinase homologue from maize roots showing light-regulated gravitropism. Planta 1996;199:18–24. 32. Luan S, Kudla J, Rodriguez-Concepcion M, Yalovsky S, Gruissem W, et al. Calmodulins and calcineurin Blike proteins: calcium sensors for specific signal response coupling in plants. Plant Cell 2002;14:S389–400. 33. Michalak M, Mariani P, Opas M, et al. Calreticulin, a multifunctional Ca2+ binding chaperone of the endoplasmic reticulum. Biochem Cell Biol 1998;76:779–85. 34. Pandey GK, Cheong YH, Kim BG, Grant JJ, Li L, Luan S, et al. CIPK9: a calcium sensor-interacting protein kinase required for low-potassium tolerance in Arabidopsis. Cell Res 2007;17:411–21. 35. Mithofer A, Mazars C, Maffei ME, et al. Probing spatio-temporal intracellular calcium variations in plants. Methods Mol Biol 2009;479:79–92. 36. Kanchiswamy CN, Malnoy M, Occhipinti A, Maffei ME, et al. Calcium imaging perspectives in plants. Int J Mol Sci 2014;15:3842–59.

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37. Ng CK, McAinsh MR. Encoding specificity in plant calcium signalling: hot-spotting the ups and downs and waves. Ann Bot 2003;92:477–85. 38. Moore CA, Bowen HC, Scrase-Field S, Knight MR, White PJ, et al. The deposition of suberin lamellae determines the magnitude of cytosolic Ca2+ elevations in root endodermal cells subjected to cooling. Plant J 2002;30:457–66. 39. Ahn SJ, Shin R, Schachtman DP, et al. Expression of KT/KUP genes in Arabidopsis and the role of root hairs in K+ uptake. Plant Physiol 2004;134:1135–45. 40. Banuelos MA, Garciadeblas B, Cubero B, Rodriguez-Navarro A, et al. Inventory and functional characterization of the HAK potassium transporters of rice. Plant Physiol 2002;130:784–95. 41. Buch-Pedersen MJ, Rudashevskaya EL, Berner TS, Venema K, Palmgren MG, et al. Potassium as an intrinsic uncoupler of the plasma membrane H+-ATPase. J Biol Chem 2016;281:38285–92. 42. Barberon M, Vermeer JEM, DeBellis D, Wang P, Naseer S, Anderson TG, et al. Adaptation of root function by nutrient-induced plasticity of endodermal differentiation. Cell 2016;164:447–59. 43. Britto DT, Kronzucker HJ, et al. Cellular mechanisms of potassium transport in plants. Physiol Plant 2008;133:637–50. 44. Chen YF, Wang Y, Wu WH, et al. Membrane transporters for nitrogen, phosphate and potassium uptake in plants. J Integr Plant Biol 2008;50:835–48. 45. Li L, Kim BG, Cheong YH, Pandey GK, Luan S, et al. A Ca2+ signaling pathway regulates a K+ channel for low-K response in Arabidopsis. Proc Natl Acad Sci USA 2006;103:12625–30. 46. Apel K, Hirt H. Reactive oxygen species: metabolism, oxidative stress, and signal transduction. Annu Rev Plant Biol 2004;55:373–99. 47. Mori IC, Schroeder JI. Reactive oxygen species activation of plant Ca2+ channels: a signaling mechanism in polar growth, hormone transduction, stress signaling, and hypothetically mechanotransduction. Plant Physiol 2004;135:702–8. 48. Yi W, Wei-Hua W. Plant sensing and signaling in response to K+-deficiency. Mol Plant 2010;3:280–7. 49. Gierth M, Mäser P. Potassium transporters in plants: involvement in K+ acquisition, redistribution and homeostasis. FEBS Lett 2007;581:2348–56. 50. Ashley MK, Grant M, Grabov A, et al. Plant responses to potassium deficiencies: a role for potassium transport proteins. J Exp Bot 2006;57:425–36. 51. Baliardini C, Meyer CL, Salis P, Saumitou-Laprade P, Verbruggen N, et al. CATION EXCHANGER 1 cosegregates with cadmium tolerance in the metal hyper accumulator Arabidopsis halleri and plays a role in limiting oxidative stress in Arabidopsis Spp. Plant Physiol 2015;169:549–59. 52. Chérel I, Michard E, Platet N, Mouline K, Alcon C, Sentenac H, et al. Physical and functional interaction of the Arabidopsis K+ channel AKT2 and phosphatase AtPP2CA. Plant Cell 2002;14:1133–46. 53. Duby G, Hosy E, Fizames C, Alcon C, Costa A, Sentenac H, et al. AtKC1, a conditionally targeted Shaker-type subunit, regulates the activity of plant K+ channels. Plant J 2008;53:115–23. 54. Fu HH, Luan S. AtKuP1: a dual-affinity K+ transporter from Arabidopsis. Plant Cell 1998;10:63–73. 55. Gambale F, Uozumi N. Properties of shaker-type potassium channels in higher plants. J Membr Biol 2006;210:1–19. 56. Fuchs I, Stolzle S, Ivashikina N, Hedrich R. Rice K+ uptake channel OsAKT1 is sensitive to salt stress. Planta 2005;221:212–21. 57. Geiger D, Becker D, Vosloh D, Gambale F, Palme K, Rehers M, et al. Heteromeric AtKC1/AKT1 channels in Arabidopsis roots facilitate growth under K+-limiting conditions. J Biol Chem 2009;284:21288–95. 58. Reintanz B, Szyroki A, Ivashikina N, Ache P, Godde M, Becker D, et al. AtKC1, a silent Arabidopsis potassium channel a-subunit modulates root hair K+ influx. Proc Natl Acad Sci USA 2002;99:4079–84. 59. Santa-Maria GE, Rubio F, Dubcovsky J, Rodriguez-Navarro A, et al. The HAK1 gene of barley is a member of a large gene family and encodes a high-affinity potassium transporter. Plant Cell 1997;9:2281–9.

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60. Jiang HB, Wang SF, Yang F, Zhang ZH, Qiu HC, Yi Y, et al. Plant growth, nitrate content and Ca-signaling in wheat (Triticum aestivum L.) roots under different nitrate supply. Plant Sci J 2015;33:362–8. 61. Schachtman D, Shin R. Nutrient sensing and signaling: NPKS. Annu Rev Plant Biol 2007;58:47–69. 62. Wilkins KA, Matthus E, Swarbrech SM, Davies JM, et al. Calcium-mediated abiotic stress signaling in roots. Front Plant Sci 2016;7 doi: 10.3389/fpls.2016.01296. 63. Schonewille JT. Magnesium in dairy cow nutrition: an overview. Plant Soil 2013;368:167–78. 64. Sreedhara A, Cowan JA. Structural and catalytic roles for divalent magnesium in nucleic acid biochemistry. Biometals 2002;15:211–23. 65. Marschner H. Mineral nutrition of higher plants. third ed. London: Academic Press; 2012. 66. Cowan JA. Structural and catalytic chemistry of magnesium-dependent enzymes. Biometals 2002;15:225–35. 67. Hermans C, Conn SJ, Chen J, Xiao Q, Verbruggen N, et al. An update on magnesium homeostasis mechanisms in plants. Metallomics 2013;5:1170–83. 68. Hermans C, Verbruggen N. Physiological characterization of Mg deficiency in Arabidopsis thaliana. J Exp Bot 2005;56:2153–61. 69. Gerendás J, Führs H. The significance of magnesium for crop quality. Plant Soil 2013;368:101–28. 70. White PJ, Broadley MR. Biofortification of crops with seven mineral elements often lacking in human diets— iron, zinc, copper, calcium, magnesium, selenium and iodine. New Phytol 2009;182:49–84. 71. Brady KU, Kruckeberg AR, Bradshaw Jr HD. Evolutionary ecology of plant adaptation to serpentine soils. Annu Rev Ecol Evol Syst 2005;36:243–66. 72. Hermans C, Vuylsteke M, Coppens F, Cristescu SM, Harren FJ, Inzé D, et al. Systems analysis of the responses to long-term magnesium deficiency and restoration in Arabidopsis thaliana. New Phytol 2010;187:132–44. 73. David-Assael O, Berezin I, Shoshani-Knaani N, Saul H, Mizrachy-Dagri T, Chen JX, et al. AtMHX is an auxin and ABA-regulated transporter whose expression pattern suggests a role in metal homeostasis in tissues with photosynthetic potential. Funct Plant Biol 2006;33:661–72. 74. Berezin I, Mizrachy-Dagry T, Brook E, Mizrahi K, Elazar M, Zhuo SP, et al. Overexpression of AtMHX in tobacco causes increased sensitivity to Mg2+, Zn2+, and Cd2+ ions, induction of V-ATPase expression, and a reduction in plant size. Plant Cell Rep 2008;27:939–49. 75. Guo W, Nazim H, Liang Z, Yang D, et al. Magnesium deficiency in plants: an urgent problem. Crop J 2016;4:83–91.

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MAGNESIUM HOMEOSTASIS MECHANISMS AND MAGNESIUM USE EFFICIENCY IN PLANTS

11 Wanli Guo

Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China

INTRODUCTION Magnesium (Mg), one of the essential macronutrient in a cell, has some specific physical and chemical characteristics.1 Mg2+ is the second most abundant intracellular cation, the fourth most abundant element in vertebrates.1 Mg is more mobile in soils as comparing to other cations, like K+, Ca2+, and NH +4. The properties of Mg2+ in soils can be ascribed to its unique chemical properties, the ionic radius of Mg is smaller (0.072 nm) than that of Ca, K, or Na, its hydrated radius is larger (0.428 nm), and has the highest charge density.1,2 Mg has diverse physiological roles in biological systems.2,3 Particularly, Mg is important to plants, 15–20% of total Mg associated with chlorophyll pigments, with its tendency to form octahedral complexes,4,5 acts mainly as a cofactor of a series of enzymes involved in photosynthetic carbon fixation and metabolism,6 and the remaining fraction stored in the vacuole.7 However, plant Mg nutrition has been consistently overlooked by botanists and agronomists in past decades, unlike other ions, such as iron (Fe) and zinc (Zn), mainly due to its abundance in soil (the 8th most abundant element on earth), high solubility in water for plant absorption, and complex functionalities.2,8,9 Surprisingly, people in many developed countries were commonly deficient in Mg, but not serious in developing countries.10 For example, the mean intake of Mg was 323 mg per day in men and 228 mg in women, values below the estimated indexes of 420 mg per days for men and 320 mg per day for women, and 10% of elderly women consumed less than 136 mg per day.3 Mg deficiency leads to severe hypomagnesemia, such as sudden cardiac death, arrhythmia, muscle dysfunction, and attention deficit disorder.11 Thus, Mg content in food crops and daily diets is an important problem for food quality and human nutrition, especially in regions where up to 75% of Mg intake daily is from cereals.12 Mg homeostasis in plant cells has been elaborated with the discovery of magnesium transporters.8,13,14 Surprisingly, genomic results indicated that the expression levels of most of the known plant Mg transporters changed little under Mg deficiency or toxicity, whereas, numerous other transporters were activated.14–17 In addition, the signaling responding to Mg stresses is largely unknown; although some reports indicated that expression levels of many genes related to phytohormone biosynthesis and signaling are significantly changed under Mg stresses.15–18 Therefore, we aimed to summarize current knowledge about plant responses to Mg stresses, Mg2+ transporters, signal transduction, interactions Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00011-9 Copyright © 2017 Elsevier Inc. All rights reserved.

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between Mg2+ and other ions, and the strategies for enhancing Mg2+ use efficiency in plants, to offer information of the Mg homeostasis in plants, and eventually to improve people’s diets for uptake enough Mg.

MORPHOGENESIS REMODELING BY Mg IMBALANCE AND THE MECHANISMS IN PLANTS Mg DEFICIENCY Generally, Mg deficient leaves have yellowish, bronze, orange-yellow, or reddish tissues between the green veins, and sometimes they have brown interveinal necrosis leaf, and the chlorosis is observed first in older leaves, interpreted as the retranslocation of Mg2+ from older to younger leaves.19,20 And high light intensity aggravates interveinal chlorosis.20 A similar result was noticed in wheat and maize plants with treatment, with heat stress, the interveinal chlorosis on their older leaves was aggravated at temperature 35°C.21 The chlorosis is mainly caused by chloroplast destruction and chlorophyll decrease. Under Mg deficient conditions, chloroplasts become round and bigger due to the accumulation of oversized starch grains with disrupted thylakoids.5 An initial decrease in Chl concentrations (Chl b firstly, followed by Chl a), the main pigments responsible for light harvesting, has been ascribed to the early accumulation of sugars in leaves, rather than to low Mg2+ levels, high accumulation of sugars in leaves may repress the expression of the CAB2 gene that is responsible for encoding Chl a and b proteins.22,23 Thus, chlorophyll catabolism can be considered as a strategy of dechelating Mg2+ from pigment molecules, as recycling Mg2+ in favor of young tissue growth,4 and was confirmed by transcriptomic results.16 Nonyellowing 1 (NYE1) and multidrug resistance protein (MRP3) was upregulated by Mg deficiency, respectively. NYE1, a chloroplast protein, initiates chlorophyll degradation during senescence, MRP3, a vacuolar ABC transporter, has transport activity of chlorophyll catabolites and glutathione conjugates.24 The transpiration is blocked under Mg deficiency before carbon accumulation, imbalance of electron transport, chloroplast destruction, etc. CO2 assimilation, stomata conductance, and transpiration were significantly lower in Mg deficient leaves than in controls, but intercellular CO2 concentration did not significantly differ between them, meaning that the lower CO2 assimilation might be not primarily caused by stomata limitation, but by the impairment of respiration under Mg deficiency.25,26 Indeed, the impairment of the linear photosynthetic electron transport was induced by Mg deficiency,5,27 and this was suggested as the main factor contributing to decrease of CO2 assimilation.27,28 The decreasing in transpiration may be the trigger for leaf death in rice under Mg deficient conditions,26 and the transpiration defect was successfully reversed by Mg2+ resupply.29 Mg-deficiency-induced increase of leaf respiration and decrease of root respiration in dark might contribute to net carbon balance by consuming the excessive carbon in leaves and saving carbon in roots.25,30 Mg2+ concentration in roots indeed decreases more rapidly than in leaves but then plateaus at a low concentration during prolonged Mg2+ starvation,15,31 indicating an efficient Mg2+ recycling mechanism to support root growth. Mg deficiency disrupts the phloem loading of sucrose from leaves into sink tissues, resulting in carbon accumulation in source leaves. An accumulation of carbohydrates in source leaves and a reduction of root growth are considered as an earlier response to Mg deficiency, since it is involved in biomass formation and carbohydrate partitioning.2 Photosynthesis and starches accumulate in young mature leaves before any decline in biomass or photosynthetic activities in common bean,32 sugar beet,22,33, and

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Arabidopsis.23 In fact, carbohydrate accumulation together with the reduction of root/shoot ratio has been reported in a variety of species under insufficient Mg supply.5 Resupply of Mg rapidly enhances sucrose export to phloem from source leaves20,23 under both dark and light conditions, indicating that the recovery of sucrose export is associated with Mg availability, not with photosynthesis. A decline in Mg-ATP concentration at the phloem-loading sites may be the major reason for inhibition of sucrose transport.34 Firstly, in Mg deficient leaves, genes in ATP biosynthesis were repressed, but ATP synthase subunit β was induced in Mg deficient roots,30 thereby preventing ATP shortage when ATP generation was decreased due to reduced respiration in roots, but vice versa in leaves. Sucrose loading into phloem is catalyzed by an H+/sucrose cotransporter, whose activity requires a proton gradient maintained by an H+-ATPase located in the plasma membranes of sieve tube cells.34 Growing evidence indicates that Mg–ATP is a major complex of ATP in cells and essential for H+-ATPase activity.35 BvSUT1, a phloemspecific proton-sucrose symporter located in companion cells of the vascular system, is induced by sucrose accumulation in sugar beet leaves under Mg deficient conditions.33,36 Similarly, some genes in both glycolysis and tricarboxylic acid cycle were also upregulated in source leaves with excess sugars.32,37 Those indicated that the plants have different ways to cope with the excess sugars under Mg deficient stress. Impairment of CO2 fixation also leads to an accumulation of unused electrons in the chloroplast, resulting in reactive oxygen species (ROS) generation and damage to chlorophyll and chloroplast membrane lipids.9,23,34 This process is due to the inhibition of the transfer of excitation energy (or electrons) from PSII to PSI in chloroplast.38 The two photosystems showed sharply contrasting responses to Mg deficiency, the downregulation of PSII through a loss of antenna, and of PSI through a loss of reaction centers, and resulted in an enhanced chlorophyll a/chlorophyll b ratio totally.22 Consequently, the electrons are transferred to protoporphyrin IX, which is associated with the light-dependent generation of ROS, such as superoxide radicals, hydrogen peroxide, and hydroxyl radicals,34 resulting in leaf chlorosis under both Mg deficient and high light conditions. In addition, Mg chelatase (MgCH) activity is reduced in the limited ATP and Mg2+ conditions. MgCH catalyzes the ATP-dependent insertion of Mg2+ into protoporphyrin IX to produce Mg-protoporphyrin IX, the first committed step in the chlorophyll biosynthetic pathway. Thus, protoporphyrin IX is overaccumulated in cells without cofactor Mg2+, and then the redundant electrons enter the process of ROS generation. Mg deficiency, contrarily, also activates antioxidative defense enzymes.16,39 The key antioxidant molecules, such as dehydroxyascorbate and oxidized glutathione, were markedly increased in Arabidopsis and rice with Mg deficient treatments, respectively.16,40 When ROS production overcomes scavenging systems, the latter cannot provide a sufficient protection to membranes against oxidative stresses.5,28,41

Mg TOXICITY Exception of Mg deficiency in some soils, and soils with excess Mg are also popular in the world. For example, the serpentine soils, formed by the weathering of ultramafic rocks, comprises of at least 70% ferromagnesian, or mafic minerals.42 Particularly, low calcium-to-magnesium ratio with significantly lower Ca2+ concentrations is the primary cause of the serpentine syndrome,42,43 and adding more Ca2+ indeed successfully alleviated the oversensitivity of the cbl2cbl3 mutant to high-Mg2+,44 indicating excessive Mg2+ may substitute for Ca2+.18,39,45 But serpentine-tolerant plants are able to survive in such high Mg2+ conditions with more developed roots, smaller shoots, smaller sclerophylls, and necrotic spots.17,42,46 Accordingly, the leaves with Mg toxic treatment turn into translucence with iodine dye

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and starch contents are substantially decreased, and accordingly, the transcriptional levels of starch biosynthesis genes, such as starch synthase 1 (SS1), SS2, granule bound starch synthase 1 (GBSS1), and ADP-glucose pyrophosphorylase large subunit 1 (APL1) are severely inhibited, whereas the starch degradation genes, such as amylase 1 (AMY1) and β-amylase (BAM1), are highly improved,18,47 suggesting that Mg toxicity induce excessive degradation of starch and sugar in leaves. In addition, the nutrients, such as Ca and Zn, are significantly decreased, whereas, the contents of manganese (Mn) and iron (Fe) were slightly increased under Mg toxicity in Arabidopsis plants.18

Mg2+ TRANSPORTERS AND Mg HOMEOSTASIS IN PLANT CELLS Comprehension of Mg2+ homeostasis in plant cells has been advanced by the discovery of some Arabidopsis Mg2+ transporters in the last decades, and these transporters might play the major roles in Mg2+ uptake, distribution, and homeostasis in cells.8 The first cloned Mg2+ transporter in plants is a Mg2+/H+ exchanger, AtMHX, encoded by a single gene in Arabidopsis, and localized in the vacuolar membrane of xylem parenchyma cells,13 that improve xylem loading in the roots and xylem unloading of Mg2+ at sink organs.8 Interestingly, transformed tobacco (Nicotiana tabacum L.) plants showed necrotic lesions and apical burnings upon growth with increased levels of Mg2+, Zn2+, and Cd2+ ions under elevated Mg2+ or Zn2+ conditions,13 and the expression of AtMHX was accordingly inhibited by high Mg2+.18 However, no change was detected in the mineral content of any organ of the transgenic plants treated with high levels of Mg2+ or Zn2+, the plants showed reduction in plant size with necrotic lesions.13,48–50 Those indicated that the necrotic lesions in transgenic tobacco are due not to high Mg2+ and Zn2+, but to imbalance of proton in cell, and the necrotic lesions is similar to those shown by plants having increased proton influx from the apoplast into the cytosol. For example, the expression and activity of the vacuolar H+-ATPase increased in the transformed tobacco plants.48–50 Those suggested that AtMHX may maintain some ions and proton homeostasis in cells, the homeostasis is essential for photosynthesis and numerous enzymatic reactions.51 Most known plant Mg2+ transporters are AtMGT (Mg transporter)/AtMRS2 genes,14,52 belonging to the superfamily of CorA-type membrane transporters.53 Ten CorA superfamily AtMGT genes in Arabidopsis were discovered, among them, AtMGT8 is a pseudogene, some are clustered into highaffinity (AtMGT1 and AtMGT10), low-affinity (AtMGT3, AtMGT7, and AtMGT9), and dual-affinity (AtMGT5) members by their affinities for Mg2+.14,52 Mg2+ transporters respond to Mg deficiency. Plants with silencing of AtMGT6,54 mutant atmgt7,55 with overexpression of AtMGT7,56 double (atmgt2/1), or triple (atmgt2/3/1) mutations57 showed phenotypic changes in response to Mg deficiency. Surprisingly, the growth retardation of double (atmgt2/1) or triple (atmgt2/3/1) mutants could be reduced by reduction of Ca2+ concentration,57 indicating the redundant role of those transporters in Ca2+ and Mg2+ homeostasis. AtMGT6 was expressed mainly in plant aerial tissues when Mg2+ levels were high in the soil, but in vascular tissues of roots under normal conditions, and was strongly induced by Mg deficiency in cortex cells and epidermal cells, including root hair.54 Its expression was highly induced in the roots (one-week-old seedlings) at 12 h after transition to the Mg2+ starved condition and then subsequently decreased,54 suggesting a role for AtMGT6 in response to the low-Mg2+ status in roots. Plasma membrane localization of AtMGT6 was shown in the root cells, while the localization either at the chloroplast or the mitochondria was implied in case of the shoot tissue.58 Thus, the silencing of AtMGT6 impaired Mg2+ homeostasis in the plants under the

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low-Mg2+ condition, and may have additional roles in aerial parts of plants. AtMGT7 is more expressed in roots and lesser in leaves, and ectopic overexpression of AtMGT7 resulted in complementation and increased biomass accumulation.55 However, AtMGT7 is located in the endoplasmic reticulum,55 indicating that AtMGT7 may act as Mg2+ homeostasis in ER and balance of Mg2+ between ER and cytoplasm. AtMGT1, a high-affinity Mg2+ transporter, localizes to the plasma membrane, involves in Mg2+ uptake in the roots, and expresses in root hairs, root elongation zones, vascular tissues, and trichomes of adult leaves.55,59 It is worth mentioning that AtMGT1 and AtMGT10 are respond to Mg deficiency, but both highly sensitive to Al.60 AtMGT10, localized to the chloroplast envelope membrane in the spongy mesophyll, stomata cells of the rosette leaves, and vascular tissues of cauline leaves, may act in roles of Mg2+ translocation to chloroplasts and chlorophyll metabolism.55,61 There are also some other candidate Mg2+ transporters, responding to Mg deficiency. Such as AtCNGC10, a cyclic nucleotide-gated channel (CNGC), mediates Mg2+ uptake, particularly in the root meristem and distal elongation zones, and long-distance transport, and may be involved in Ca2+ and Mg2+ transport, which, in turn, regulates K+ (and Na+ under salinity) transport.62 Some Mg2+ transporters may involve in homeostasis of Mg2+ in cells or subcellular organelles. The tonoplast-localized AtMGT2 and AtMGT3 are believed to be involved in Mg2+ homeostasis in mesophyll cells,45 consistently with their expression in both rosette and cauline leaves,55 as is AtMHX,8 which maintains cation balance in the vacuole. The mitochondrial membrane-localized protein AtMGT563 and two ER-target proteins, AtMGT964 and AtMGT4,65 are essential for normal pollen development, and homozygous mutant plants were not obtained from all three mutant lines. AtMGT4 is expressed notably in pollen grains from bicellular pollen stage to mature pollen stage,65 and in developing seeds, the later is confirmed with that this gene is colocalized with a quantitative trait locus (QTL) for seed Mg2+ concentration.66 AtMGT9, a low-affinity Mg2+ transporter, is expressed evenly distributed throughout the anther early in development, is then concentrated in the tapetum, and is also detectable in vascular tissues of the leaves, and in young roots.64 AtMGT5, a dual-functional Mg-transporter, operates in a concentration-dependent manner in the mitochondria, namely it serves as an Mg-importer at micromolar levels and facilitates the efflux in the millimolar range. Examination of two independent T-DNA insertional mutants of AtMGT5 gene demonstrated that pollen formation was strongly impaired in atmgt5 hemizygous mutants,63 suggesting a critical role for Mg2+ transport between cytosol and mitochondria in male gametogenesis in plants. However, AtMGT5 is expressed in very few parts of the plant,55,63 indicating it cannot bear the Mg2+ flux in mitochondria of the whole plant. Confusedly, no significant phenotypes were observed for single-gene knockouts of three genes (AtMGT1, AtMGT2, and AtMGT3). Likewise, no impairment of plant growth and development was observed for two double knockout lines that were created (atmgt2 atmgt3 and atmgt1 atmgt3), even in spite of strong and overlapping gene expression early in seedling development under the Mg deficiency.55 Meanwhile, the expression levels of the known Mg transporters changed little in transcriptomic analysis in Arabidopsis and rice,15–17,58 but some were activated by Mg toxicity.18 The expression levels of AtMGT1, AtMGT7, and AtMHX were upregulated in the wild type though nonsignificantly under high Mg2+,17 and eight AtMGTs and AtMHX were responding to Mg toxicity.18 Those results were consistent with that six Mg2+ transporters were highly expressed in the mesophyll and some could be correlated with higher accumulation of Mg2+ in plants under serpentine conditions.63 Those indicated that most of Mg2+ transporters are more sensitive to Mg toxicity than to Mg deficiency, and some further studies need to carry out to demonstrate those phenomena.

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IMBALANCE OF Mg HOMEOSTASIS IN PLANTS IMBALANCE OF Mg HOMEOSTASIS BY SOME STRESS FACTORS The availability of Mg2+ to plants depends on various factors: the distribution and chemical properties of the source rock material, and its grade of weathering, site specific climatic and anthropogenic factors in agricultural systems. The source rock material from which it originates is that containing different types of silicates, and Mg contents depend on silicate types (muscovite > biotite > hornblende > augite > olivine).20 In addition, Mg2+ concentration varies significantly with latitude in soil and in organisms at different tropic level, and Mg2+ in ecosystems will vary with climate changes.67 Mg2+ is highly prone to leaching, and leaching is considered as a key factor affecting Mg2+ availability for roots.22,30 The Mg2+ requirement for optimal plant growth is 1.5–3.5 g kg−1 in vegetative parts, and Mg2+ concentrations in soil solutions are considered between 125 mM and 8.5 mM for sufficient supply to plant growth,7,20,68 however, plants encounter Mg2+ stresses commonly. Mg2+ deficiency occurs generally in plants growing in acidic soils (high H+ levels) with low cation-exchange capacity, and about 70% of the potentially arable land on earth is acidic.69 Mg2+ has the low binding strength of Mg2+ to the soil colloids, because of its large hydrated radius, and this is considered to be an important factor influencing Mg2+ phytoavailability in shallow or coarse-textured soils.70 However, MgCO3 formation and excess Ca2+, K+, and Na+ in alkaline soils also reduce Mg2+ availability to crops.71 In addition, droughty soil69 and some Al toxic fields72,73 also inhibit Mg2+ absorption by roots. Among some competing elements in soils, such as K, Ca, Al, and NH4,8 Mg2+ is the least taken up nutrient. High temperature and high rainfall in tropical regions also lead to Mg2+ leaching, and reduce balances between plant Mg2+ concentration and Mg2+ availability.67 Wheat and maize plants were also highly susceptible to heat stress when grown under low Mg supply.12 The highest activities of superoxide dismutase (up to 80 % above the control), glutathione reductase (up to 250 % above the control), and ascorbate peroxidase (up to 300 % above the control) were measured when Mg deficient plants were subjected to heat.21 Thus, plants suffering from Mg deficiency have a higher susceptibility to heat stress, and ensuring a sufficiently high Mg2+ supply for crops is an important requirement for minimizing heat- and radiation-related losses in crop production. Low pH (pH 1 mM and high-affinity transport systems (HATS) that function when N concentration in the soil  N > S. Recently a number of studies involve RE3+ complexes with biologically important molecules.73,77 In addition, the similarity between metal

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ions existing in living organisms (mainly, Ca2+ and Mg2+ ions) and RE3+ ions allows their replacement by these latter ions and subsequently structural information can be obtained by luminescence techniques. The rare earth ores only occur as REn+ ions, where the predominant oxidation state is trivalent (Ln3+, Sc3+, and Y3+), but divalent (Sm2+, Eu2+, Tm2+, and Yb2+) and tetravalent states (Ce4+, Pr4+, and Tb4+) also exist. The rare earths are associated with various biological effects, justifying their use as fertilizers. Some evidence has indicated that rare earths in low concentrations can promote the gain of biomass and plant growth. However, in many studies in China, rare earths were used in a combined manner, which made it difficult to understand the role of individual elements in this process.78 In addition, for a long time there was no analytical methodology sensitive enough to detect and quantify the low concentrations of metals in biological systems. The appearance of techniques, such as inductively coupled plasma—mass spectrometry has allowed advances in the rare earths area.79 However, there are still few studies that explore a molecular basis for the biological effects caused by these chemical elements. Treatments with RE at low concentration have shown an increase in crop and rice yields (Oryza sativa).80 For Ginkgo biloba, treatment with 50 mg L−1 lanthanum nitrate led to an increase in the production of proteins, soluble sugars, and total flavonoids in leaves of this plant of 33, 29, and 48.6%, respectively. Growth of plant cell culture can also be favored in the presence of low rare earth concentrations. Wang et al.81 observed that 10 µM cerium can increase growth, polysaccharide biosynthesis and carbon, nitrogen, and phosphorus utilization by Dendrobrium houshanense culture. The key to increasing biomass and growth of plants and plant cell cultures appears to be the rare earth concentration range used. In addition to promoting growth, some papers reported that treatment with rare earth can improve the seed germination rate,82 growth of roots,83 and increased the absorption of minerals and metals present in the soil.84 Various effects may also be cited for the use of rare earths in agriculture, among them, stimulate chlorophyll synthesis,85 promote seedling development86,87 and improve bioavailability of Ca and Mn in soil.87 Another important factor for the growth of plants is photosynthesis. There is evidence that the presence of Ce3+ ion may increase photosystem II activity in spinach and that Ce3+ may also accelerate photosynthetic reactions in vivo.88 It has already been shown that some RE can bind to chlorophyll in Dicranopteris dichotoma. Spectral analyses revealed that lanthanum can coordinate with the porphyrin rings of chlorophyll and that the La–chlorophyll complex can form bilayer structures. A more recent study89 confirms the presence of rare earths in chloroplasts and chlorophyll of D. linearis. Although rare earths are not essential chemical elements for living organisms, they can influence the uptake and metabolism of other nutrients. For example, it has been shown that lanthanum can, in a manner analogous to calcium, inhibit potassium uptake into plants when applied for a short period of time. Some studies show that plants treated with 1 µM lanthanum may have a decrease in calcium uptake into roots.90 Depending on the plant studied, RE may cause an increase or inhibition of phosphorus uptake, and changes in the levels of magnesium, manganese,91 and nitrogen sources. Wu et al.92 demonstrated an increase in the production and excretion of taxol in Taxus spp. after being cultivated with rare earths in the concentration range from 1.15 to 23.0 µM. Other works have shown the beneficial effect of rare earths in the production of flavonoids,93 glycosides,94 and alkaloids.95 One explanation for this would be that rare earths may be involved in increasing membrane permeability, thus leading to increased excretion of secondary metabolites, and may also be involved in the activation of transcription of genes related to secondary metabolite biosynthesis. In other hand, rare earths can also play a role in increasing resistance to stress. Studies have shown that lanthanum increases water use efficiency of plants grown under conditions of water limitation.

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301

Also, the application of rare earths may increase the resistance of plants subjected to acid rain and low temperatures and high concentrations of metals in the soil. Erbium and Terbium may have a plant protective effect against aluminium toxicity.96 The mechanism involved in this protection by rare earths involves the stimulation of the synthesis of malate, which chelates aluminium. In addition, variations in the antioxidant system of rare earth treated vegetables suggest an induced resistance to oxidative stress conditions. d’Aquino et al.82 explored how the enzymatic and nonenzymatic antioxidant systems of Triticum durum varied as the plant was treated with different rare earths in different concentrations, resulting in an increase in production of some antioxidants in certain parts of the plant, such as the root. Studies in different crops explored the application of rare earths mixed with nitrate. This enabled, for example, an increase in N uptake in wheat crops and an increase of 37.4% in corn crops. The application of the rare earth mixture with nitrate also brought benefits not only in the growth of the crop but also aided in the germination, photosynthesis, and increase in the production of hormones as IAA. The use of this mixture allowed an increase in CO2 assimilation in a beet crop, thus increasing the photosynthetic rate. In wheat, soybean, corn, and beet crops, it was possible to observe an increase in chlorophyll production. Another benefit to using rare earth was in germination. Wheat seeds treated with rare earths and nitrate increased in germination rate during winter. In addition, the application of the nitrate mixture with REs, provided an increase in phosphorus absorption in wheat and rice crops.97

CONCLUSIONS Improving global crop productivity and food quality, together with protection of environmental quality, are the main challenges for the immediate future. Due to high economic and environmental costs of the industrial process for nitrogen fertilizer production, combined with the increase in food demand, it is necessary to cultivate crops that can remove the nutrients applied to soils efficiently, but it is also important to develop plants that are efficient at internal nutrient transport, storage, mobilization and remobilization, and therefore require less fertilizer. NUE is an essential prerequisite for future crop production. In relation to nitrogen and NUE, two strategies are being used, precision agriculture and genetic engineering with roots as the primary target. In this chapter, we present approaches that support the use of endophytic bacteria in nonnodulating plants and the application of rare earth as fertilizers as two more alternatives to improving NUE. The effects of endophytes and rare earths on modification of root architecture and induction of genes related to nitrate, ammonium transporters and nitrogen assimilation become interesting research options in addition to transgenic approaches and direct gene transfer in breeding new cultivars.

ACKNOWLEDGMENTS Paolo Di Mascio thanks PFAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo, No. 2012/12663-1, CEPID Redoxoma No. 2013/07937-8), CNPq [Conselho Nacional para o DesenvolvimentoCientífico e Tecnológico, No. 301307/2013-0, No. 159068/2014-2 and (Hermi Felinto de Brito, No. 490242/2012-0)] and PRPUSP (Pro-Reitoria de Pesquisa da Universidade de São Paulo, NAP Redoxoma No. 2011.1.9352.1.8) for financial support. Miguel J. Beltrán-García gratefully acknowledge the financial support from CONACYT: Proyectos de Desarrollo Cientifico para atender Problemas Nacionales (CONACYT 212875), CONACYT (2016-269607) Apoyo al

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Fortalecimiento y Desarrollo de la Infraestructura Científica y Tecnológica and Project 207400 of Bilateral Cooperation Mexico-Brazil funded by CONACYT and CNPq (Brazil, No. 490440/2013-4). G M-R and A H-R thanks CONACYT for PhD fellowship #256660 and #414739, respectively. We are also grateful for support from the John E. and Christina C. Craighead Foundation, USDA-NIFA Multistate Project W3147, and the New Jersey Agricultural Experiment Station. We also acknowledge Yuritssi Garcia-Ochoa and Ana Mejia for their technical contribution for Figs. 16.1 and 16.4 and Yur Chavez-Castrillon for preparation of all figures presented.

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INTRODUCTION TO GWAS AND MutMap FOR IDENTIFICATION OF GENES/QTL USING NEXT-GENERATION SEQUENCING

Kenji Yano, Yoshihiro Ohmori, Toru Fujiwara University of Tokyo, Tokyo, Japan

INTRODUCTION Crop production is greatly influenced by environmental factors, such as temperature, photoperiod, and soil moisture.1 One of the most important factors influencing plant growth is soil macronutrient uptake and utilization efficiency because plants cannot grow normally and survive without each of the macronutrients. Economic benefits to farmers can be maximized through controlling the optimum range for nutrient availability to plants. In general, farmers attempt to alleviate nutrient deficiencies and boost yields by using fertilizer when nutrient availability in the soil is limited. At the same time, farmers tend to overapply fertilizers because crop production is very sensitive to nutrient deficiencies. Excess use of fertilizers will result in soil degradation and contributes to water pollution. Therefore, the elucidation of the mechanisms underlying plant responses to nutrient deficiencies is very important for nutrient management. However, plant responses to multiple nutrient stresses including limited macronutrients availability are complex and involve the induction of specific genes.2 To understand such responses effectively, plant response mechanisms must be studied at the molecular level. Many researchers have found significant differences in macronutrient use efficiency (MUE) among different germplasm.3,4 Phenotypic variation in the targeted traits is necessary for molecular markerbased genetic analysis. Maker-based applications, such as DNA fingerprinting, linkage mapping, and quantitative trait loci (QTL) mapping have become more sophisticated with the advent of different genotyping platforms.5 In 2005, next-generation sequencing (NGS) technologies were first introduced to the experimental market and have been utilized for genome-wide genotyping by mapping sequence reads against a reference genome in various species in a highly efficient way, allowing genome-wide association studies (GWAS) and MutMap.6,7 They are powerful approaches for identification of the causal genes/QTL responsible for a given phenotypic diversity in the population (pooled samples treated as germplasm collection). However, NGS datasets are too large and complex to permit direct use by plant biologists. Presently, the challenge for plant biologists is how to effectively use these resources for molecular marker-based genetic analyses. A simple convenient statistical analysis of GWAS datasets is well established through Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00017-X Copyright © 2017 Elsevier Inc. All rights reserved.

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application suites like Plink8 or GeneABEL,9 but we need to carefully design experiments to eliminate the effects of confounding factors that can be present in population structure analyses. We summarize recent successful examples of genomics studies and introduce conventional and modern approaches for candidate gene cloning and the development of experimental platforms.

GWAS EXPERIMENTS IN PLANTS The identification and characterization of the genes are important for both understanding the genetic basis of phenotypic variation and crop breeding through genetics. Over the past 10 years, QTL analysis has been a powerful tool for identifying genetic loci involved in phenotypic differences between two parents.10 However, QTL mapping with two parents is limited because of restricted allelic diversity and low-recombination rates. GWAS has also used in mapping QTLs related to morphological and physiological traits, such as plant growth, yield, and tolerance to stresses.6 The advantage of GWAS over traditional QTL analysis is that it provides much more data on genetic variations, allowing increased evaluation of recombination and better mapping resolution, because it can identify associations between nucleotide polymorphisms and phenotypic variation by using more than 100 plants showing genetically diverse variation.11 Nearly 10 years ago, GWAS was developed for studying human genetic diseases and for breeding livestock. So far, this approach has successfully identified many loci or genes associated with human genetic diseases and desirable livestock traits.12 After new genes are identified, the information can be utilized for the prevention and treatment of human diseases or better production of livestock. Although GWAS is a powerful approach to study the relationship between genetic variants and traits, it has some problems. First, complex population structure often causes a spurious relationship between the genotype and phenotype; second, total phenotypic variation cannot be genetically explained by the variants that GWAS identifies because almost all phenotypes are controlled not only genetically but also environmentally. Different environmental conditions should produce different phenotypic outcomes, which seriously affect GWAS. GWAS in plants could be controlled better than for human genetic diseases because we can control growth conditions to minimize complex environmental factors. For example, we can repeatedly study the phenotype of genetically identical samples and examine them at the exact same time of day under controlled greenhouse conditions. In addition, we can also minimize the effects of genetic factors through crossbreeding. By crossing individuals from genetically distinct strains, genetic variation is randomized in the progeny population. This situation reduces the risk of false positive and negative associations, and provides more reliable candidate loci involved in our traits of interest. Consequently, GWAS is now routinely applied and has been more successful in plants than humans.11

GWAS FOR MACRONUTRIENT USE EFFICIENCY GWAS of nitrogen use efficiency Nitrogen (N) is a primary component of vitamins, nucleic acids, proteins, amino acids, and energy systems. Nitrate (NO −3 ) or ammonium ( NH +4 ) can be readily absorbed by microorganisms, and therefore, interspecific competition can occur between plants and microorganisms. N is one of the most limiting nutrients in crop production, and the improvement of nitrogen use efficiency (NUE) in plants is necessary to sustainably ensure an increase in the world’s crop production. NUE is defined as the grain

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yield obtained per unit of available N in the soil.13 NUE is divided into two components, the uptake efficiency (N uptake per unit of available N) and the utilization efficiency (grain yield per unit of N uptake). In various crops and wild plants, genetic and phenotypic diversity for the uptake efficiency and the utilization efficiency have been found.14 Moreover, as a result of many long-term breeding efforts, the selection of better NUE varieties led to increased genetic variation for both factors of NUE.15 In wheat (Triticum aestivum L.), efforts have been made to identify chromosomal regions with NUErelated traits.16 GWAS in wheat was performed by using 214 European elite varieties, 28 NUE-related traits, and 23,603 SNPs, and it identified 333 genomic regions associated with 28 NUE-related traits, including 11 loci for N uptake efficiency and 6 loci for N utilization efficiency. This study was the first GWAS on NUE-related traits in cereal grains and it shed light on the selection pressure on yield and the effects of recent breeding on NUE. Symbiotic nitrogen fixation (SNF) is one of the major sources of N for crop production, and it has been estimated that N fixed by bacteria of the genus Rhizobium ranges from 200 to 500 kg ha−1 in the case of many leguminous plants.17 As most plants only use N from the soil, N fertilizers are one of the most expensive inputs for farmers in Africa and Latin America. Biological N fixation makes it possible to maintain high bean yields at a low cost with minimal N fertilization. However, our understanding of the genetic architecture involved in the control of SNF remains incomplete. In Andean bean (Phaseolus vulgaris L.), GWAS was conducted to explore the genetic architecture of SNF.18 Andean bean is able to fix 20–60 kg N ha−1 in tropical environments.19 Andean beans are the most widely planted bean in Africa and are often used as a natural source of N in modern agriculture.20 Thus, enhancing our knowledge of SNF and its genetic control can promote breeding for increased SNF ability in Andean beans. In the GWAS experiments, a subset of the Andean diversity panel comprising 259 beans grown in the greenhouse and the field was used to identify the genomic regions associated with improved SNF. Rhizobia infects the roots of legumes to form root nodules and converts N or molecular dinitrogen (N2) from atmosphere. Plant roots infected by rhizobia produce a lump on the root called a nodule. In the study, the visual nodule score was evaluated by measuring the number of nodules for each individual. An SNP for nodule score was detected on chromosome Pv09 in the field experiment. Interestingly, this SNP was also important for the proportion of total N derived from the atmosphere (%Ndfa) in the shoot biomass and the seed, which were used as the primary method to evaluate SNF, because rhizobia fix N gas from the atmosphere. This result indicated that this SNP has pleiotropic effects on these traits. The detection of pleiotropic associations is one of the advantages of GWAS and may increase the understanding of physiological mechanisms underlying phenotypic variation. On chromosome Pv09, a candidate gene for a leucine-rich repeat receptor-like protein kinase (LRR-RLK) was identified. LRR-RLKs have been reported to play an essential role in legume–rhizobia nodulation processes.21 It is plausible that this LRR-RLK is related to nodule formation and N fixation in P. vulgaris.

GWAS of phosphorus-deficiency-tolerance traits Phosphorus (P) is also one of the important nutrients for crop production, and global crop production relies heavily on P fertilizers. P in soils is tightly bound with metal cations (e.g., calcium, magnesium, and zinc), so it is largely unavailable to plants in spite of total amounts of P in soils that are generally adequate to sustain plant growth. P deficiency is often a major limiting factor to plant growth.22 Thus, improving P uptake, internal P-use efficiency, and tolerance to P deficiency in plants will allow farmers to increase crop production. In a recent study, a subset of the broad rice

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association panel including 71 indica, 56 aus, and 69 tropical japonica was used to evaluate tolerance to P deficiency and to detect putative loci associated with root efficiency (RE), which is the efficiency for P acquisition per unit root size.4 Subspecies- or variety-specific GWAS for RE detected associations below the threshold of P < 1.0E-04 on chromosomes 1, 6, 7, and 12, which were confirmed in the indica subspecies but not in the aus or tropical japonica varieties because they lacked SNP variation. These results suggested that the indica subspecies possesses loci for moderately high RE; therefore, it may be possible to engineer high-yielding cultivars through the utilization of donor genotypes conferring high RE. In Aegilops tauschii Coss. (Tausch’s goatgrass), GWAS of P-deficiency-tolerant traits was performed using a population of 380 specimens grown in hydroponic experiments with and without P application.23 Association analysis using a mixed linear model identified nine markers associated with the P deficiency tolerance index (PDTI). PDTI is the ratio of traits under no-applied P condition to traits under applied P. VERNALIZATION2 (VRN2), a major gene involved in the vernalization response, was identified as a candidate gene that might be related to P-deficiency tolerance. In addition, candidate genes encoding defense response proteins, enzymes, transcription factors, and storage proteins were identified. These data will provide the foundation for breeding P-deficiency-tolerant cultivars. A. tauschii can cross with tetraploid wheat and beneficial genotypes of A. tauschii can be incorporated into the wheat genome. Therefore, identified genes in A. tauschii can also contribute to wheat breeding.

IDENTIFICATION OF GENE VARIANTS USING ASSOCIATION TEST FOR IMPROVING MACRONUTRIENT USE EFFICIENCY As mentioned previously, genotypic differences for MUE have been observed in many crops. These differences make it possible for breeders to develop cultivars that have a high tolerance to macronutrient deficiencies through the selection of the most superior genotypes. Traditionally, plant breeders have indirectly selected plants with superior genotypes of a trait of interest based on visual appraisal. However, this process requires knowledge, experience, and skill in practical breeding and can be slow and costly. On the other hand, plant breeders can now use molecular markers that are tightly linked to target loci (i.e., QTL) responsible for the genotypic differences. This plant breeding strategy is called marker-assisted selection (MAS). In general, however, the use of MAS for tolerance to macronutrient deficiencies is limited; the genetic basis of plant adaption to low soil nutrient levels remains poorly understood, and wider analysis of natural allelic variants in cultivated and wild populations is rare.24 Recently, research by Zhang et al. provided one example in which attempts were made to dissect the mechanisms of a major QTL (TOND1) that confers tolerance to N deficiency in rice.25 In this study, an analysis of the association between N-deficiency tolerance and variants in the regulatory and coding regions of TOND1 was performed using 45 rice cultivars. This analysis identified five SNPs that are associated with N-deficiency tolerance, and they might contribute to the increase in N-deficiency tolerance in cultivars harboring the superior TOND1 allele. The variants in the target gene can result in phenotypic variation, so association analysis or GWAS is useful for finding the appropriate allele(s) that has the potential to enhance the trait of interest. MAS can be utilized to effectively transfer the appropriate alleles into different cultivars. Therefore, with the identification of a growing number of genes and variants involved in MUE, plant breeding via MAS will be facilitated.

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A SUITABLE POPULATION FOR QTL MAPPING AND GWAS So far, traditional QTL mapping and GWAS has successfully provided many QTL associated with important traits in various plants.4,18,23 However, QTL mapping with two parents is limited because of restricted allelic diversity. Although GWAS overcome the limitations of biparental crosses, difficulties in identifying true associations within a complex population structure have remained problematic. The high genomic diversity in a diverse set of germplasm may cause spurious associations. To perform QTL mapping and GWAS efficiently, it is therefore necessary to select suitable populations that are both genetically unstructured and interrelated. In this context, alternative approaches for QTL mapping using a nested association mapping (NAM) population or multiparent advanced generation intercross (MAGIC) populations are quite appropriate. A NAM population is created by crossing an inbred reference line to multiple inbred lines; whereas a MAGIC population is generated by randomly and sequentially intercrossing multiple lines.26 In maize, for example, a NAM population consisting of 25 recombinant inbred lines successfully identified multiple candidate genes related to disease resistance.27 Huang et al. developed a MAGIC population derived from four elite wheat cultivars and performed QTL analysis for mapping loci involved in plant height and hectoliter weight.28 In rice, Bandillo et al. demonstrated that a MAGIC population was useful for identifying genes and QTL associated with submergence tolerance and resistance to bacterial blight.29 The success of these populations depends on two major advantages over populations with high genomic diversity. First, mapping QTL can be performed for multiple traits derived from multiple parents; second, multiple recombination events in populations increase the resolution of mapping. Although association studies using NAM or MAGIC should be powerful, it takes a long time to produce these populations, which is a big disadvantage of these techniques. In the case of GWAS, there is no problem in terms of population production, but population structure is still problematic. Structured populations are often observed in independent natural populations that inhabit different environments with selective pressures, because adaptations to a given environment can be induced by changes in allele frequencies at multiple loci. When adaptive traits are highly correlated with population structure, GWAS identifies many false positives. In these cases, gene flow and the geographic structure of natural populations must be considered to improve detection of true signals. High levels of population structure are also found in cultivated crop species that are selected for their adaptation to local conditions or that meet the local needs of breeders.30 In contrast, plant breeding uses interbreeding of distantly related individuals to develop new crop varieties with desirable properties. This process leads to novel rearrangements of alleles and increases the rate of recombination. By using a crossbreeding population, we can detect the true associations because the unbiased allele distribution avoids the confounding effects of population structure. Using local breeding populations, some researchers have successfully identified the causal genes that contribute to agronomically important traits.31 In addition to the above, linkage disequilibrium (LD) should be taken into account when charactering the mapping resolution for GWAS. LD is the nonrandom association between alleles at adjacent loci. During meiotic cell division, genetic recombinations between homologous chromosomes reduce the association between alleles. As a result, many meiotic events cause LD decay. However, LD depends not only on recombination events but also on the mating system of plants. For example, as maize is an outcrossing plant, the extent of LD is generally small and approximately 2000 bp on average, which is enough to identify underlying genes.32 On the other hand, LD in self-pollinating plants, such as rice and soybean often extends several hundred kilobases.33 Larger LD in self-pollinating plants reduces association mapping resolution. To obtain high mapping resolution, we have to increase the

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population size or increase its genetic diversity, which also increases the confounding effects from the diverse structure of the genome. Thus, additional analyses are required for populations with slower LD decay (discussed below).

GENOTYPING RESOURCES Recent advances in NGS technology have enabled us to genotype and accurately resequence large-scale populations in a cost-effective manner. To date, several NGS-dependent techniques, such as reduced representation libraries, restriction site–associated DNA sequencing, and genotyping-by-sequencing (GBS) have been developed.34 These cost-effective techniques have been successfully applied to several plants. For a panel of 304 soybean lines, a GBS approach was used to provide dense genome-wide markers, and it identified several loci associated with complex traits, such as maturity, plant height, seed weight, seed oil, and protein. Soybean is a self-pollinating plant, so the number of markers available from GBS is sufficient to detect the associations due to its slow LD decay. Although this study indicated that GBS provides a much higher density of markers than is required for GWAS, it is possible that causal loci are within gaps that do not have a suitable marker density.35 Genotyping based on whole-genome sequencing (WGS) has also been used for model species, such as Arabidopsis thaliana, rice, maize, grape, soybean, and poplar. WGS can provide greater density genotyping data than GBS.36 This advantage allows identification of regions of intense recombination and provides the opportunity to determine LD structures at maximal resolution. WGS also enables the capture of rare variants that might be missed in GBS. It is known that high proportions of SNPs that are predicted to be functional are categorized to variants with a low minor allele frequency.37 Therefore, WGS might be needed to detect rare variants when the goal is to identify causal variant(s).

A STRATEGY FOR IDENTIFICATION OF CAUSAL GENES/VARIANTS GWAS can simultaneously identify the wealth of genetic variants that are associated with various complex traits (Fig. 17.1). Previous studies have been successfully identified association signals that

FIGURE 17.1  Schematic of Typical GWAS Work Flow

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explain large proportions of phenotypic variation. However, the estimation of relevance between the causal variant(s) and phenotypic variation is elusive for the inclusion of many significant variants given the extent of LD; it is for this reason that the pursuit of causal variants and causal genes has been limited in plants. Therefore, further postprocessing steps are required to identify the causal genes. Here, we describe the combined use of GWAS and statistical approaches, computational analysis, and expression profiling to narrow down the candidate genes. DNA segments with strong LD, termed haplotype blocks, have been generated by mutation and maintained by population history. In strong LD, DNA segments have low rates of recombination and genetic polymorphisms tend to remain correlated; LD blocks could be used to infer the distribution of recombinant events (Fig. 17.2). Although screening of candidate regions with LD blocks is effective for narrowing down candidate variants, LD is typically insufficient to select plausible candidate variants within a candidate region. Further analysis is needed to separate the candidate causal variants from those with meaningful associations. Functional information from polymorphisms can certainly support further analysis of the candidate variants. Several polymorphism annotation programs (SnpEff, ANNOVAR, and the ENSEMBL VEP module) have been developed and widely used for this purpose.38–40 In rice, Yano et al. recently reported that polymorphisms annotation is effective for prioritizing candidate variants.31 They attempted to identify genes associated with agronomic traits using GWAS based on WGS and classified all polymorphisms in a candidate region for each agronomic trait into five groups (I–V). In these groups, group I included the polymorphisms that were significantly associated with trait variation in GWAS and predicted to induce amino acid substitutions or to change splicing

FIGURE 17.2  Linkage Disequilibrium Around a Mutation In this figure, two ancestral chromosomes (top: black and white) generate six different chromosomes (bottom). The mutation that occurs in an ancestral chromosome is indicated by a red triangle and the adjacent chromosomal variants are shown in black and white, according to the two ancestral chromosomes. These are shuffled by recombination during meiosis. Chromosomal variants that are physically close tend to remain associated with the ancestral mutation, even though recombination gradually removes small regions of chromosome variants.

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junctions. They preferentially focused on the polymorphisms classified in group I and finally identified four new genes associated with flowering date, panicle number, awn length, and plant height. It is of course possible that a nonsynonymous polymorphism causes an amino acid substitution, which may have an effect on the protein structure and the phenotype, which in turn suggests such a polymorphism as the causal variation. If the nonsynonymous polymorphism lies in a highly conserved region, based on the comparison of sequence alignments of multiple species, the gene with the nonsynonymous polymorphism will be the best candidate gene. Therefore, polymorphism annotations and multiple sequence alignments are the most valuable approaches for providing a very strong functional hypothesis.

INTEGRATION OF GWAS AND GENE EXPRESSION DATA FOR RAPID IDENTIFICATION OF CAUSAL GENE Although polymorphisms that induce amino acid substitutions are reasonably expected to be the causal loci associated with traits, polymorphisms in the regulatory region of genes must be taken into consideration as well. Expression information will establish whether any of the variants within the association signal are associated with gene expression variation. Multiple studies have identified genomic variants that alter the specific gene expression associated with traits, highlighting their utility in understanding the mechanisms underlying GWAS results.41–43 For instance, Si et al. recently performed GWAS of grain size in a diverse collection of world rice germplasm.44 This study identified a candidate gene for grain size by comparing gene expression levels, which were measured with reverse transcription polymerase chain reactions, of varieties with contrasting phenotypes. When a quantitative trait is regulated by differential gene expression, expression information will be useful for predicting the target genes. The expression information within a specific candidate region is efficient but does not cover all genes in the genome. Several high-throughput techniques have been developed for transcriptome profiling. RNA sequencing and microarray analyses are the most commonly used technologies for performing highthroughput analysis of transcript abundance. These platforms provide comprehensive information of differential gene expression and make it possible to perform gene expression quantitative trait loci (eQTL) mapping. Regulatory elements or other gene regions containing variants that influence gene expression are considered eQTL. As with GWAS, the aim of eQTL mapping is to find the association between polymorphisms and gene expression. Depending on its proximity to the gene being regulated, an eQTL can be classified into two groups. The majority of eQTL are cis-acting (cis-eQTL), located in the same place as the regulated gene, but some are trans-eQTL, which are located at a different position from the gene being regulated. eQTL studies with model plants, such as A. thaliana, rice, and maize showed that cis-eQTL have a relatively large effect on local expression levels, whereas trans-eQTL have global influences on gene regulation.45–48 Recent eQTL mapping studies have revealed novel associations between gene expression patterns and biological processes.48,49 In addition, multiple studies in human diseases have provided strong evidence that GWAS signals are enriched with eQTL.50–52 When cis-eQTL are localized in GWAS candidate regions, genes with notable cis-eQTL will indicate good candidate genes that might have effects on both gene expression and trait variation. A combined approach using GWAS and expression information is based on the assumption that QTL are regulated by differential expression of candidate genes. This assumption is not always true because genetic variants in coding genes may produce defective proteins. Therefore, the combination of eQTL and polymorphisms annotation will complement each other and be useful for predicting the candidate genes underlying GWAS.

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NGS-BASED MAPPING-BY-SEQUENCE APPROACH FOR GENE IDENTIFICATION OF MUTANTS IN RICE NGS methods allow mass sequencing of genomes and enable researchers to explain phenotypic diversity in terms of the allele diversity of a genome. As described previously, NGS-based GWAS is an effective approach for rapid detection and identification of genes/QTL that define phenotypic diversity of MUE among natural populations. Forward genetic approaches, which isolate mutants of interesting phenotypes and map causal genes for mutant phenotypes, are also crucial for understanding the genetic factors of MUE. In general, the resolution of genetic mapping depends on the density of molecular markers, and causal mutations are typically distinct from these markers. Hence, local sequencing within mapped regions of a genome are always required to find the actual causal mutations after genetic mapping.53 NGS can also be a powerful method for genetic mapping because it allows identification of high-density molecular markers as SNPs and indels from the whole genome and can reveal actual changes in the nucleotide sequence in mutants at the same time.34,54 In this section, we introduce three mapping-by-sequence approaches, MutMap, MutMap-GAP, and MutMap+, as methods that accelerate gene identification from rice mutants using NGS.7,55,56 Although bioinformatics software is need to analyze NGS data, pipelines including script files and detailed procedures to run MutMap and MutMap+ are provided by the Iwate Biotechnology Research Center (http:// genome-e.ibrc.or.jp/home/bioinformatics-team/mutmap), and we can easily apply these methods for our own research.

MutMap Abe et al. explained the scheme of MutMap for gene identification from a rice mutant pool that was mutagenized by ethyl methanesulfonate (EMS),7 and here we describe the main principle of MutMap (Fig. 17.3A). In MutMap, a recessive mutant from a mutant pool is backcrossed to a wild-type plant. The resulting F1 plant is self-fertilized, and the F2 progeny (>100 plants) are screened to select the plants that show a segregated mutant phenotype (>20 plants). DNA of mutant plants from F2 progeny is pooled, and WGS is performed on NGS machines (e.g., Illumina GAIIx sequencer). Then sequence reads are mapped to a reference genome to detect SNPs. In this context, it is expect that the SNPs of sequence reads will be 50% mutant type and 50% wild type in genomic regions that are not linked to the mutant phenotype. Meanwhile, the SNPs in genomic regions that are linked to the mutant phenotype should be 100% mutant type. SNPs frequency is converted to a SNP index that represents the ratio between the number of sequence reads that have a mutant SNP and the total number of the sequence reads corresponding to the SNP. The SNP index, therefore, would be equal to 1 near the causal gene and be 0.5 for the unlinked genomic region. Scanning the SNP index across the genome, we can identify the genomic region that has clusters of SNPs with a SNP index of 1 as a candidate region that harbors a causal mutation of the mutant phenotype. Subsequently, SNPs with a SNP index of 1 in the cluster should be examined in detail with genomic annotation to identify the causal gene that corresponds to the mutant phenotype. MutMap has a particular advantage in gene identification from crop plants because it would be impractical to handle a large F2 population in the field, and only a small F2 population (>100 plants) is required for MutMap. Moreover, MutMap does not require crosses different from a conventional crossing scheme for genetic mapping, and therefore, can detect genes with minor phenotypic effects that are often masked by crosses of more distantly related lines.57,58 Agronomically important traits of crops,

FIGURE 17.3  Illustration of the MutMap Methods (A) General outline of MutMap. The EMS-mutagenized mutant is backcrossed with a wild-type parental plant. The resulting F1 plant is self-fertilized and generates F2 progeny. Mutant phenotype plants (>20) from F2 progeny; >100 are collected to extract pooled DNA. DNA is sequenced by NGS, and sequence reads are mapped to a reference genome to detect SNPs. The SNP index is calculated, and clusters with a SNP index of 1 are searched as candidate regions associated with the causal mutation. (B) Gap identification by MutMap-Gap. In MutMap-Gap, reads unmapped to the reference genome are collected and used for de novo assembly with reads that have been mapped around the candidate region with MutMap. Then mutant bulk are reads mapped on scaffolds to detect SNPs and calculate the SNP index. The gap region in the candidate region will have SNPs that show a SNP index of 1. (C) General outline of MutMap+. MutMap+ does not require artificial crosses. In MutMap+, two types of bulk samples (mutant and wild type) are sequenced and used calculate the SNP index. By subtracting the SNP index of mutant and wild-type bulk samples, the candidate region can be searched when the ∆SNP index is 0.5. Asterisk indicates Nipponbare genome sequence is usually used as a reference genome in rice. If a wild-type parental line and a mutant are not Nipponbare, SNP conversion is required to avoid false peaks (SNP index of 1) in MutMap and MutMap-Gap.

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including MUE, are usually controlled by QTL that have a small phenotypic effect. MutMap will provide an efficient gene isolation method for crop plants using mutagenesis-based genetic screening.

MutMap-GAP MutMap allows the rapid gene identification of mutants by WGS and SNPs detection against a reference genome. Gene identification in MutMap depends on the quality of the reference genome. If the genetic background of the mutant has structural differences from reference genome, causal mutations in the genome regions that are missing in the reference genome cannot be identified. MutMap-Gap is a method to detect structural variations in the mutant genome using targeted de novo assembly; it enables gene identification from missing genome regions (gaps) in a reference genome when combined with MutMap55 (Fig. 17.3B). In MutMap-Gap, a candidate genome region, corresponding to the mutant phenotype, which has a cluster of SNPs with a SNP index of 1, is detected with MutMap, and then gaps between the reference genome and mutant genome in the candidate region are searched as follows. First, using sequence reads derived from a wild-type plant that shares the parental background of the mutant, the sequence reads that did not map to the reference genome are collected. Second, scaffolds are made by combining sequence reads that have mapped near the candidate region and the collected unmapped reads in a de novo assembly. Third, sequence reads derived from the bulk DNA of mutant F2 progeny are mapped to scaffolds to calculate the SNP index. Finally, the scaffolds that have a SNP index of 1 are searched to identify the causal mutation. In the case of rice, a genome sequence of one japonica cultivar, Nipponbare, is usually used as the reference genome.59 Rice has many cultivars/landraces and has considerable genetic variation.60,61 MutMap-Gap enables us to adapt NGS-based genetic mapping for any genetic background of rice cultivars to identify the genes of interest.

MutMap+ Both MutMap and MutMap-Gap methods are based on a collection of mutant-type plants among backcrossed F2 progeny. If mutants are lethal or sterile and cannot produce backcrossed F2 progeny, these two methods cannot be used to identify genes. MutMap+ has been developed to address this problem56 (Fig. 17.3C). In MutMap+, they used M3 progeny that segregate the wild-type phenotype and mutant phenotype instead of backcrossed F2 progeny. Then they made two bulk DNA samples, one from 20 to 40 M3 plants that showed the mutant phenotype and another from 20 to 40 M3 plants that showed the wild-type phenotype. These two bulk DNA samples were separately sequenced with a NGS machine (e.g., Illumina GAIIx sequencer), and mapped to the reference sequence to detect SNPs and calculate the SNP index following the MutMap method. In the bulk mutant DNA, however, there are two cases when genomic regions have a SNP index of 1. The first case is when the region with a SNP index of 1 actually harbors the causal mutation. The second case is when the region with a SNP index of 1 is caused by M2 genotypes, which means the SNPs are fixed homozygous in the M2 generation and are shared by all M3 plants. To eliminate this second case, the SNP index of the wild-type bulk sample is subtracted from that of the mutant to calculate the ∆SNP index. The ∆SNP index value should be around 0 for most of the genome, but it should have a large positive value in the region harboring the causal mutation. As MutMap+ produces a genetic mapping without artificial crossing, it is broadly adaptable for rapid gene identification not only in rice but also other crops that are difficult to artificially cross.

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SUITABLE MUTANT RESOURCES FOR MutMap ANALYSIS IN RICE As described previously, mutant gene identification using MutMap is based on SNPs detection. Although there are many genetic resources for functional analysis of the rice genome, which was generated by many different methods,62 the method used to generate a mutant is important for adapting MutMap methods for rapid gene identification. The OsCAO1 gene for a paler green phenotype,7 the Pii gene for a blast resistance phenotype,55 the OsNAP6 gene for an early stage lethality phenotype,56 and the hst1 gene for a salt-tolerant phenotype63 were all identified from an EMS mutant pool of the rice cultivar Hitomebore (Oryza sativa L. subsp. japonica) by MutMap methods. Hence, it is desirable that mutants be generated by mutagens, such as EMS and N-methyl-N-nitrosourea (MNU), that cause single nucleotide base substitutions with a high mutagenesis rate.64,65 Among mutant collections that are available worldwide, Oryzabase distributes more than 6000 MNU-mutagenized mutant strains with phenotypic data (http://shigen.nig.ac.jp/rice/oryzabase/). These mutant strains would be a useful source of mutants for identifying genes that are associated with MUE.

CONCLUSIONS AND FUTURE PROSPECTS Recently, high-throughput sequencing technology has become a powerful tool in the field of genetics. NGS facilitates mass collection of genomic information and has opened the next genomic era, which will enable researchers to form a bridge between phenotypes and genotypes. In this chapter, we described GWAS and MutMap for utilization of NGS to rapidly identify genes/QTL from natural variants and mutants. QTL control many agronomically important traits, and therefore, GWAS can provide key information to disclose the phenotypic contribution of complicate gene combinations for plant breeding. MUE is an agronomically important trait. Although many QTL that are related MUE are reported, genes of QTL are less frequently identified and their molecular mechanisms have not been elucidated. Recent improvements in the cost and speed of NGS have made it easy to obtain a whole-genome resequence data. GWAS and MutMap with the high-resolution NGS data would accelerate gene identification from QTL that have an important role in MUE and increase our understanding at a molecular level. In addition, NGS has enabled genomic research in nonmodel plant species.66 Wild relatives of crop species are adapted to grow under diverse environments and have natural traits, such as efficient water and nutrient utilization and resistance to microorganisms and pests that confer selective advantages.67 In the future, wild relatives may be requisite materials for identification of genes/QTL to improve MUE in crops. On the other hand, phenotypic data that reflect morphological and physiological changes of plant tissue in response to MUE, such as changes in shoot/root architecture and in the transcriptome and metabolome, will be also required for understanding the mechanisms of MUE in plants. Bioinformatics tools that integrate these mass data will be crucial for future research.

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28. Huang BE, George AW, Forrest KL, et al. A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotechnol J 2012;10(7):826–39. 29. Bandillo N, Raghavan C, Muyco PA, et al. Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 2013;6(1):11. 30. Filippi CV, Aguirre N, Rivas JG, et al. Population structure and genetic diversity characterization of a sunflower association mapping population using SSR and SNP markers. BMC Plant Biol 2015;15:52. 31. Yano K, Yamamoto E, Aya K, et al. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet 2016;48(8):927–34. 32. Wilson LM, Whitt SR, Iba AM, et al. Dissection of maize kernel composition and starch production by candidate gene association. Plant Cell 2004;16(10):2719–33. 33. Gupta PK, Rustgi S, Kulwal PL. Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol 2005;57(4):461–85. 34. Davey JW, Hohenlohe PA, Etter PD, et al. Genome-wide genetic marker discovery and genotyping using nextgeneration sequencing. Nat Rev Genet 2011;12(7):499–510. 35. Huang YF, Poland JA, Wight CP, et al. Using genotyping-by-sequencing (GBS) for genomic discovery in cultivated oat. PLoS One 2014;9(7):e102448. 36. Boutet G, Alves Carvalho S, Falque M, et al. SNP discovery and genetic mapping using genotyping by sequencing of whole genome genomic DNA from a pea RIL population. BMC Genom 2016;17:121. 37. Zhu Q, Ge D, Maia JM, et al. A genome-wide comparison of the functional properties of rare and common genetic variants in humans. Am J Hum Genet 2011;88(4):458–68. 38. Cingolani P, Platts A, Wang LL, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 2012;6(2):80–92. 39. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38(16):e164. 40. McLaren W, Gil L, Hunt SE, et al. The Ensembl variant effect predictor. Genome Biol 2016;17(1):122. 41. Cui J, Stahl EA, Saevarsdottir S, et al. Genome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritis. PLoS Genet 2013;9(3): e1003394. 42. Nica AC, Dermitzakis ET. Using gene expression to investigate the genetic basis of complex disorders. Hum Mol Genet 2008;17(R2):R129–34. 43. Conde L, Bracci PM, Richardson R, et al. Integrating GWAS and expression data for functional characterization of disease-associated SNPs: an application to follicular lymphoma. Am J Hum Genet 2013;92(1): 126–30. 44. Si L, Chen J, Huang X, et al. OsSPL13 controls grain size in cultivated rice. Nat Genet 2016;48(4):447–56. 45. DeCook R, Lall S, Nettleton D, et al. Genetic regulation of gene expression during shoot development in Arabidopsis. Genetics 2006;172(2):1155–64. 46. West MAL, Kim K, Kliebenstein DJ, et al. Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics 2007;175(3):1441–50. 47. Horiuchi Y, Harushima Y, Fujisawa H, et al. Global expression differences and tissue specific expression differences in rice evolution result in two contrasting types of differentially expressed genes. BMC Genom 2015;16:1099. 48. Holloway B, Luck S, Beatty M, et al. Genome-wide expression quantitative trait loci (eQTL) analysis in maize. BMC Genom 2011;12:336. 49. Ranjan A, Budke J, Rowland SD, et al. eQTL regulating transcript levels associated with diverse biological processes in tomato. Plant Physiol 2016;172(1):328–40. 50. Zhou K, Yee SW, Seiser EL, et al. Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat Genet 2016;48(9):1055–9.

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CHAPTER

TRANSGENIC APPROACHES FOR IMPROVING PHOSPHORUS USE EFFICIENCY IN PLANTS

18

Hayato Maruyama*, Jun Wasaki** *Hokkaido University, Sapporo, Japan **Hiroshima University, Higashi-Hiroshima, Japan

INTRODUCTION Phosphorus (P) is an essential element for plants. In general, fertilization of P is necessary for cultivated lands because of the low mobility of P in the soils. Nevertheless, the economically available P rock resources are estimated as sufficient only for the next several decades.1 Exhaustion of P sources presents a grave threat to sustainable crop production. Furthermore, less than 20% of fertilized P is available for utilization by crops in the first year after application.2 Therefore, agricultural workers tend to input excess P fertilizer, which accumulates in arable lands by fixing and adsorption with multivalent cations, such as aluminum (Al), calcium (Ca), and iron (Fe) in the soil.3–5 Organic P also occupies a large proportion of unavailable P in soils. To achieve sustainable food production corresponding to the population explosion taking place on Earth, the improvement of P use efficiency of crop plants can contribute to the decrease of P fertilizer input. Plants frequently face P deficiency. Therefore, plants have evolved many adaptations to low concentrations of available phosphate in the soil. Adaptation mechanisms can be categorized into two strategies: the efficient use of internal P and enhancement of P uptake from the rhizosphere. Many genes involved in the internal P use efficiency and P acquisition can be regarded as candidates for the improvement of P availability of plants by genetic manipulation. As described in this chapter, examined trials and perspectives for transgenic approaches to improve P use efficiency are discussed.

IMPROVEMENT OF P UPTAKE EFFICIENCY BY ROOT FUNCTIONS PLANT STRATEGIES TO MOBILIZE AVAILABLE P IN SOILS Enhancement of P uptake includes the following strategies: (1) degradation of organic phosphate (Po) in the rhizosphere by secretory acid phosphatases (APases),6,7 (2) solubilization of sparingly soluble inorganic P (Pi) by exudation of organic acid and decrease of rhizosphere pH,8,9 (3) increase of Pi uptake rate by upregulation of high-affinity Pi transporters,10,11 (4) increase of root surfaces by modification of root architectures, such as increase of lateral root formation and root hairs,12–15 and (5) symbiotic Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00018-1 Copyright © 2017 Elsevier Inc. All rights reserved.

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strategies with mycorrhizal fungi.16 A certain group of plants, such as white lupin and Proteaceae, forms unique dense root clusters under P-deficient conditions. The benefits of the unique root architecture, so-called “cluster roots” or “proteoid roots,” are not only the increase of root surfaces but also to liberate Pi in the rhizosphere.8,17–19

TRIALS TO USE NONSPECIFIC ACID PHOSPHATASES SECRETED FROM ROOTS Almost all P in soils exists as Po.20 In actuality, P plays important roles in organisms as many Po forms, such as sugar phosphates, phospholipids, nucleotides, and phosphorylated proteins. Plants are only able to uptake P as orthophosphate via phosphate transporters. Consequently, organic P is an unavailable form in the rhizosphere soil. To mobilize the Po in soils, it must be hydrolyzed by phosphomonoesterase (phosphatase) activities. Plants have the ability to release APase into the rhizosphere. It is expected to improve the ability to mobilize Po in soils by transgenic approaches because the ability to secrete APase varies considerably among plant species.6 Plants have many phosphatase genes in their genome. For instance, Arabidopsis (Arabidopsis thaliana) has 29 members of the purple acid phosphatase (PAP) family.21 Results demonstrated that only two PAPs are predominant as the secreted APase in Arabidopsis.22 Among nine crop plants, white lupin (Lupinus albus L.) showed the highest APase activity in root exudates.6 A main isoform of the APase secreted from white lupin was purified and characterized.23 This enzyme has numerous benefits for activity in rhizosphere soils, such as wide substrate specificity, stability for wide ranges of pH and temperature, and a very low Km value (27 µM for p-nitrophenyl phosphate).23–26 In fact, when the partially purified enzyme was injected into tomato (Solanum lycopersicum L.) and sugar beet (Beta vulgaris L.), their P uptake was improved compared to that of noninjected plants.27 We have isolated the gene for APase secreted from roots of white lupin and designated it as LASAP2.28 Overexpression of LASAP2 in tobacco (Nicotiana tabacum L.) plants increased APase activity in the root exudate and improved the P uptake from Po supplied as soluble phytate-P in aseptic culture.29 Also, LASAP2 overexpressing line showed higher growth and P accumulation from three types of soil collected with low P fertilities.29,30 However, the increase of P uptake from applied phytate-P was limited by insolubility in soils. Other nonspecific APase overexpressing plants have been generated. MtPAP1, a root-secreted APase of Medicago truncatula Gaertn., was transformed into Arabidopsis under CaMV35S promoter.31 This experiment also confirmed increases of APase secretion and P uptake from soluble phytate. AtPAP10 overexpressed Arabidopsis lines showed higher growth by increased P accumulation from ADP as a sole Po source in their media.32 Similarly, growth and P accumulation of PvPAP3 overexpressed Arabidopsis were higher than WT in MS medium containing dNTPs without Pi.33 A soybean line overproducing AtPAP15 showed stimulation of P accumulation in both acid and calcareous soils.34

TRIALS USING ROOT-SECRETED PHYTASE Organic P occupies a significant amount of unavailable P in soils. Especially phytate, which is a phosphate ester of inositol, is the most abundant Po (20%–50%) in soils.35,36 It was long believed that phytate-P in the soil was only slightly available because root-secreted APases usually showed low relative activity against inositol phosphate.25 Then, many trials to enhance of utilization of phytate-P in the soils were conducted by introduction of phytase (phytate-specific phosphatase) isolated from microorganisms.

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When phyA of Aspergillus was overexpressed in Arabidopsis under the control of CaMV35S promoter, P acquisition from soluble phytate in agar media was increased significantly.35 An attachment of signal peptide of an extensin from carrot at the reader sequence of phyA (ex::phyA line) resulted in secretion of the transformed phytase from roots, although the phyA line (without the signal peptide) only accumulated the internal activity. Phytate-P accumulation in ex::phyA line was not only higher than wild type but also higher than phyA line. The construct was also transformed into subterranean clover (Trifolium subterraneum). The transformant showed an increase of P accumulation from phytate supplied to agar media.37 The effect of phyA transformation increased more by the control of promoter of pht1;2, a high-affinity Pi transporter, than by simple overexpression.38 Although ex::phyA transformed tobacco (N. tabacum) lines showed very high (3.7-fold) P accumulation from phytate in sterile agar medium, limited increase of P acquisition in soil condition (up to 52%) was demonstrated.39 A synthesized phytase gene was introduced by Zimmernann et al.40 into potato (Solanum tuberosum) under regulation of the promoter of LeEXT1.1 gene, a root specific gene. They showed an increase of P uptake by transgenic plants cultivated in the soil, although the increase of plant growth was not significant. Introduction of β-propeller phytase of Bacillus subtilis was also examined.41,42 Expressed β-propeller phytase without signal peptide produced increased plant growth by the internal phytate-P recycling.42 It was also reported that root-secreted β-propeller phytase improved the phytate-P uptake from media.41 Similarly, overexpression in Arabidopsis of PHY-US417, a phytase from B. subtilis, enhanced P acquisition from phytate-P from media.43 Recently, some trials have been conducted to examine plant-derived phytase for phytate utilization. Ma et al.44 transformed a phytase gene MtPHY from M. truncatula in Medicago sativa. Results show that growth of transformed lines was increased under sand culture conditions supplied with phytate. Similarly, overexpression of OsPHY1, a gene for phytase, improved growth and P accumulation in tobacco plants grown under sand culture conditions supplied with phytate as the sole P source.45 GmPAP4, a PAP member isolated from soybean (Glycine max), had higher phytate-degrading activity.46 Overexpression of GmPAP4 in Arabidopsis increased plant growth and shoot P content by sand culture supplied with phytate.46 Root exudates of natural plants also show phytate-degrading activities. For example, root exudates of tobacco reportedly showed 18.2% phytase activity against APase activity.47 Consequently, plants are able to obtain P from “soluble” phytate.48 However, solubility of phytate is normally very low in soils; moreover, the P uptake from phytate applied in soils was extremely limited, also in phytase transformants.37,47 Turner hypothesized that the chemical property of Po is accompanied by the rate of accumulated P in soils.49 Especially, it is understood that phytate is the most abundant Po form in soils because of its ready fixation and stabilization in soils. In other words, this fact implies that not only hydrolysis by phytase but also solubilization by chelating agents are extremely important to increase phytate-P utilization in soils.

SOLUBILIZATION OF SPARINGLY SOLUBLE INORGANIC PHOSPHATE BY ORGANIC ACID EXUDATION The Pi concentration in soil solution is generally very low. The insolubility of Pi is the result of its chemical properties: it is easily absorbed with metal ions, such as Al, Ca, and Fe in the soil mineral, producing insoluble precipitates. The insoluble Pi must be solubilized for plant acquisition. Organic acids released from roots have chelating ability with metallic ions, which form sparingly soluble compounds

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with inorganic phosphate in the soils. Major organic acid forms are citrate and malate. Citrate shows the highest mobilization among popular organic acids because of its high chelating ability.50 Rates and molecular species of root-secreted organic acids vary considerably. Consequently, genetic modification can improve plants’ ability to secrete organic acids. Possible key processes involved in organic acid exudation are carboxylate metabolism and carboxylate-specific transport systems. The strategy for organic acid exudation is also necessary for Al tolerance because of organic acid chelating ability with Al ion. Consequently, several trials to enhance organic acid exudation have been targeted at improving Al stresses. Citrate has three carboxyl groups in the molecule. Therefore, it has higher chelating ability than monocarboxylates or dicarboxylates. Consequently, the effect of citrate overproduction on Al stress was examined by overexpression of citrate synthase (CS) of Pseudomonas aeruginosa.51 A positive effect of citrate overproduction on the Al tolerance was found in tobacco and papaya (Carica papaya) plants. In CS-overexpressing tobacco plants, P accumulation also increased compared with the wild type.52 Other researchers reported that an increase of citrate exudation occurred by CS overexpression.53–55 Recently, it has been shown that CS-overexpressed plants exhibit increased citrate exudation from roots and P uptake from Fe–P in soils.56 To increase organic acid exudation, other strategies have been applied. It has been shown that repression of isocitrate dehydrogenase (ICDH), which is involved in the degradation of citrate in TCA cycle, caused an increase of internal citrate concentration.53 Overexpression of malate dehydrogenase (MDH) of alfalfa (M. sativa) caused an increase of exudation of various organic acids and subsequently P accumulation in acid soils.57 Similarly, MDH of Penicillium oxalicum overexpressed tobacco showed higher malate exudation and enhanced P accumulation from Al–P, Fe–P, and Ca–P.58 In the case of GhmMDH1 from cotton (Gossypium hirsutum L.), overexpressed cotton showed higher malate exudation and stimulation of P uptake from sparingly soluble forms.59 It has been suggested that phosphoenolpyruvate carboxylase (PEPC) supports the synthesis of organic acids from the phosphorylated carbohydrates produced in the glycolytic pathway.60,61 Begum et al.62 confirmed that transgenic rice of maize PEPC showed a greater increase of oxalate exudation and accumulated P than the wild type. Tesfaye et al.57 reported that organic acid exudation and P accumulation were not influenced by overexpression of alfalfa PEPC. Because organic acids are indeed important as basal metabolites, modification of metabolisms involved in organic acids might produce unexpected effects. In contrast to the positive result of CS overexpression by de la Fuente et al.,51 one experiment showed that citrate exudation and Al tolerance were not influenced by overproduction of citrate.63 As the alteration of internal citrate concentrations and citrate efflux were largely insensitive to drastic changes in either CS or ICDH activities, they concluded that other factors, such as transport out of the roots, control citrate efflux.53 Transporters are required to release organic acids from roots. In recent decades, studies of carboxylate transporters have shown huge progress, especially for Al stress responses. It was clarified that ALMT- and MATE-type transporters are, respectively, essential for malate and citrate transport from roots to the rhizosphere.64–66 It has been suggested that exudation of malate is crucially important for Al tolerance of wheat (Triticum aestivum).66 Overexpression of TaALMT1, the responsible gene of wheat, increased malate exudation and subsequently improved the Al tolerance of barley (Hordeum vulgare).67 Furthermore, one report has described that P accumulation in TaALMT1 overexpressed plants grown in acidic soil was higher than in wild type.68 In the case of citrate, which has greater potential to improve P availability in the soils, it is expected that identification of transporter genes and the introduction will elicit high-level improvement of citrate exudation. It has been shown that the citrate-permeable anion

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channel is involved in citrate exudation from protoplasts prepared from white lupin cluster roots.69 Uhde-Stone et al. isolated a multidrug and toxin extrusion protein as a candidate transporter using macroarray analysis targeting cluster root–specific expressed genes in white lupin.70 Such information can serve as a stepping stone to enhance citrate exudation. Giles et al.71 tried to use FRD3, a MATEtype citrate transporter induced by Fe deficiency, to ascertain whether P accumulation was increased or not in overexpressed tobacco line. Results show that the higher exudation in FRD3 line caused higher P accumulation from sparingly soluble P forms. Organic acid exudation from roots under low-P conditions accompanies acidification of the rhizosphere.9 It has been suggested that H+-pyrophosphatase plays important role on the rhizosphere acidification under P-deficient conditions.72 P-starvation-induced AVP1 for type I H+-pyrophosphatase of Arabidopsis caused subsequent induction of P-type ATPase. Overexpression lines of AVP1 exhibited enhanced rhizosphere acidification. Interestingly, organic acid exudation from the roots was increased by AVP1 overexpression in Arabidopsis, tomato, and rice. Their results imply that coordinate of pumping mechanisms with specific transporters is important to increase organic acid exudation by transgenic approaches. Nicotianamine aminotransferase (NAAT) was clarified as a key enzyme in the synthesis pathway of mugineic acids, which plays an important role in the Fe uptake by Poaceae plants.73 Introduction of the NAAT gene of barley into rice increased mugineic acid synthesis and subsequently improved the tolerance to Fe deficiency, which is frequently critically important in alkaline soils.74 In the case of NAAT, it was transformed with the native promoter, which could be induced only under the necessary conditions. The selections of promoter driving the key genes must be regarded as improving nutrient dynamics in the rhizosphere using a bioengineering approach. In soils, both Pi and Po are easily fixed and immobilized. APase and phytase are able to act with soluble substrates. Consequently, the chelating ability of carboxylates in the root exudates is important for Po mobilization. Addition of carboxylates into a soil caused the increase of soluble Pi but its rate was high in simultaneous addition with APase.75 This evidence suggests that both APase for hydrolysis of phosphomonoester and organic acid for solubilization are important for mobilization of sparingly soluble Po. Fig. 18.1 presents a summary of the utilization of sparingly available P in soils by plants. Both insoluble Pi and Po must solubilize via the chelating function of organic acids. APase is accessed to solubilized Po and produce Pi by their enzyme activity. Mobilized Pi is assimilated by high-specificity Pi transporters located in root surfaces. Gene modification is a promising method to enhance the mobilization of unavailable P, although further studies must be conducted to achieve efficient expression in the roots by specific promoters and utilization of both functions by chelators and phosphatases.

INCREASE OF Pi UPTAKE RATE BY EXPRESSION OF HIGH-AFFINITY Pi TRANSPORTERS High-affinity Pi transporters play an important role in Pi acquisition from the rhizosphere. Mitsukawa et al.76 reported that the Pi uptake rate of cultured tobacco cells was increased by the overexpression of PHT1, a high-affinity Pi transporter of Arabidopsis. Overexpression of NtPT1, a highaffinity Pi transporter of tobacco, under control of the ubiquitin promoter of maize (Zea mays), also improved the Pi uptake in rice (Oryza sativa) plants.77 Overexpression of OsPht1;4 caused significantly higher Pi accumulation in roots, straw, and brown rice.78 Similarly, overexpression of OsPT2,

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FIGURE 18.1  Mobilization of Sparingly Available P in the Rhizosphere (1) Exudation of organic acids via organic acid transporter; (2) solubilization of sparingly soluble organic P by chelating function of organic acids; (3) solubilization of sparingly soluble inorganic P by chelating function of organic acids; (4) transport of cell wall–binding APase; (5) transport of secretory APase; (6) hydrolysis of organic P by APases; (7) inorganic P uptake via Pi transporter.

a Pi transporter of rice, under control of CaMV35S promoter improved the Pi uptake and growth of soybean.79 In OsPT2-overexpressed soybean, N accumulation under P-deficient conditions was also enhanced.80 Overexpression of HORvu;Pht1;1, a high-affinity Pi transporter of barley, driven by rice actin promoter does not significantly enhance the Pi uptake rate of barley.81 They described the possible influence of post-transcriptional mechanisms on transporter activity. It is particularly interesting that suppression of OsPht1;4 by RNAi caused a decrease of Pi concentration in straw and brown rice and mRNA accumulation of OsPHR2, a MYB transcription factor (TF)-regulating Pi homeostasis.78 Some unknown post-transcriptional regulation system might be important in P-deficiency responses.

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INCREASE OF Pi UPTAKE RATE BY ROOT ARCHITECTURE MODIFICATION Plants grown under low-P conditions are well known to exhibit modified root architecture, such as suppression of primary root elongation, stimulation of lateral root emergence, and increased root hair density. The alterations of root architecture by low-P stress are the acclimation responses to improve P uptake efficiency. A P-deficient inducible β-expansin, GmEXPB2, can promote root cell division and elongation.82 GmEXPB2 overexpressed soybean and rice showed higher root growth and improved P efficiency.34,83 Overexpression of HDA19 for a histone deacetylase increased not in root hair length but in root hair density.84 Reports of the relevant literature also suggest that HDA19 affected SPX domain–containing proteins, and it is also involved in lipid remodeling. LaGPX-PDEs for glycerophosphodiester phosphodiesterase of white lupin involved in the acclimation to P-deficient condition through both function in regulation of root hair development and enhanced glycerophosphodiester turnover.85 The root architecture modification might be connected tightly with the lipid metabolism under P-deficient conditions. An important QTL for P-deficiency tolerance in indica rice cultivar has been characterized as Pup1.86,87 Recently, the responsible gene Pstol1 for a protein kinase was isolated from Pup1 locus.88 It was demonstrated that Pstol1 contributed to the root system enlargement, and that overexpression increased the growth and Pi accumulation. Application of Pstol1 gene transfer to local cultivars worldwide is expected to support high yields of rice in low-P soils. Some low-P tolerant species, such as the family Proteaceae and genus Lupinus in legumes, form an unique root structure, so-called cluster roots, under low-P conditions.18 Cluster roots are consisted with highly dense rootlets within 1 cm in length gathered in narrow parts of secondary roots, and show “exudative burst” of P mobilizing substances, such as organic acids and APase.7,9 Citrate is the main carboxylate in exudates of mature cluster roots of white lupin grown under low-P conditions.89 Reportedly, white lupin had complex strategies to protect citrate from consumption by rhizosphere microorganisms: they included acidification of the rhizosphere to repress bacterial growth, flavonoid exudation to induce sporulation for fungi, and chitinase and glucanase secretion to lyse the cell walls of fungi.90 Although key genes for cluster root formation and their specific function remain unclear, utilization of the mechanisms of cluster roots might dramatically improve P uptake efficiency.

IMPROVEMENT OF INTERNAL P USE EFFICIENCY MODIFICATION OF CARBON METABOLISMS Efficient use of internal P can include the following strategies: (1) bypassing the sugar metabolisms for repression of Pi consumption,61 (2) degradation of organic phosphate by nucleases and phosphatases,91,92 (3) substitution of phospholipids with sulfolipids and glycolipids,93–95 and (4) retranslocation of Pi from older tissues to newer tissues.96 Carbon metabolism frequently interacts with P nutrients, as suggested by transcriptomic analysis.95 For example, starch in leaves is frequently accumulated under low-P conditions because phosphate is a critical component of leaf starch metabolism.97 Overexpression of AtPAP2, a member of PAP in A. thaliana, enhanced starch accumulation by the modification in sugar and TCA metabolisms.98–100 Increase of starch content in food materials is important, although consideration of the influence in P metabolism is lacking in experiments using the AtPAP2 overexpression line. Actually, a decrease of sucrose concentration was found in AtPAP2 overexpressed lines.99 Sucrose is recognized as the

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systemic signal for low-P response from shoots. Therefore, overexpression of AtPAP2 must be considered influencing not only C metabolism but also P nutrition. Lipid remodeling is a general response in plants grown under low-P conditions. Phospholipids constitute a large Po pool. Therefore, phospholipids are degraded under P-deficient conditions. Mobilized P will be reused as the urgent P-containing compounds. As evidence to explain the importance of lipid remodeling for low-P tolerance, knockouts of both phosphatidate phosphohydrolase (PAH) 1 and 2 genes showed critical growth repression under P starvation.101 However, it has not been reported that any genes directly involved in lipid metabolisms can have enhanced low-P tolerance by transgenic approaches. Leaves of family Proteaceae distributed in P-impoverished soils decreased phospholipids and increased galactolipids and sulfolipids by leaf development.102 The importance of maintaining the photosynthetic rate per unit of P content has also been suggested.103 These results suggest that understanding and manipulation of the gene for upstream of lipid remodeling is better for use in the improvement of P use efficiency than the single gene modification involved in lipid remodeling.

OPTIMIZATION OF SIGNALING NETWORKS INVOLVED IN P STRESS RESPONSES The complex signaling network regulates the adaptation strategies for low-P stress. Actually, PHOSPHATE STARVATION RESPONSE REGULATOR 1 (PHR1), a MYB family TF, was the first gene isolated as a TF involved in P stress responses.104 Recently, many other TFs involved in low-P response, such as MYB family, bHLH family, WRKY family, and MADS family, have been isolated.105–111 Furthermore, it has been revealed during the last decade that noncoding RNA and MAPK cascade play important roles on P stress responses.112–115 The signaling networks for low-P responses found in Arabidopsis is summarized in Fig. 18.2. At the promoter region, PHR1 regulates the genes harboring P1BS sequences, which are frequently found in the promoter sequence of P starvation inducible genes.116 phr1 mutant showed critical reduction of growth under low-P conditions. Consequently, it has been concluded that PHR1 plays a central role in P stress responses.104 Overexpression of PHR1 enhanced the growth and P accumulation and expression level of known P-responsive genes.108,117,118 In fact, yield increases by overexpression of the wheat ortholog, TaPHR1 have been demonstrated.119 Overexpression of the rice ortholog, OsPHR2, caused growth inhibition by excess accumulation of P,120,121 which suggests that overexpression of PHR might not be suitable to improve low-P tolerance. It has been also revealed that SPX1 is a phosphate-dependent inhibitor of PHR1 via direct interaction.122 To maintain P homeostasis, functional regulation of PHR type TF might be necessary to improve P use efficiency. Yi et al.105 isolated a novel bHLH-type TF involved in P stress responses from rice. They designated the TF as OsPTF1. The result clarified that OsPTF1 was expressed constitutively but induced in the roots under low-P conditions. Growth amounts of OsPTF1 overexpressed rice were increased 30% and 20%, respectively, by improvement of P accumulation under low-P conditions by hydroponic and soil culture conditions. Similarly, overexpression of TabHLH caused an increase of P accumulation.123 Although no difference in P concentration was found, growth and P mass accumulation were increased in TaMYB1 overexpressed Arabidopsis and in TaMADS51 overexpressed tobacco.110,111 Growth and P uptake of rice overexpressing OsWRKY74 were increased more than 16%.109 It is particularly interesting that OsWRKY74 is involved not only in P but also in Fe nutrients.109 Because transcriptomic analysis

 Improvement of internal P use efficiency

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FIGURE 18.2  A Model for Signaling Pathway for P Deficiency in Arabidopsis Expression of PSI (P starvation induced) genes is regulated by many transcription factors (TFs), such as MYB, WRKY, and bHLH. The SPX genes, IPS1, and microRNAs (miR156, miR399, miR778, and miR827) are regulated by PHR1. WRKY 42 and 45 also regulate PSI genes. Su(var) 3–9 homologs 6 (SUVH6) encoding a histone H3 lysine 9 (H3K9) methyltransferase is regulated by miR778. SQUAMOSA promoter–binding protein-like 3 (SPL3) is downregulated by miR156.

also indicated crosstalk between P and Fe,94 OsWRKY74 might be involved in transcriptional crosstalk between P and Fe. Several noncoding RNAs play important roles in low-P acclimation. miR399 was isolated from Arabidopsis as the first miRNA induced by low-P stress.124 miR399 causes degradation of mRNA for PHO2, encoding a ubiquitin-conjugating E2 enzyme and negatively regulating PHO1 function125; it also maintains Pi homeostasis. Recently, it was clarified that NLA (NITROGEN LIMITATION ADAPTATION) protein had similar function of PHO2 on P homeostasis, and the transcripts were cleaved by miR827.126 Overproduction of miR399 causes excess Pi accumulation to a toxic level.114 However, TPSI1/Mt4 family mRNAs, such as AtIPS1, At4, and OsPI1 are the low-P inducible lncRNA (long noncoding RNA) molecules containing the complementary sequence of miR399.127 Overexpression of the lncRNA decreased the Pi concentration by inhibiting the role of miR399 and maintaining the function of PHO2.128 Results clarified that a cis-natural antisense of PHO1;2, which is a xylem loading Pi transporter, acts as a translational enhancer.129 Overproduction of miR778, a low-P inducible miRNA, caused increased P starvation inducible genes, such as miR399 and PHT1;4 for Pi transporter and subsequently increased growth under low-P conditions.130 Similarly, P uptake was increased by overexpression of miR156, which was also low-P inducible miRNA, and induced some P stress responsive genes.131 Results show that P demand might be limited to a minimal amount if suitable Pi homeostasis could be achieved through regulation of complex RNA functions in the future.

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CONCLUSIONS AND FUTURE PERSPECTIVES Improvement of low-P tolerance is important to sustain food production and to reduce the need for consumption of limited P resources. Transgenic approaches constitute a valuable methodology, as shown by many studies for specific genes for improving sparingly available P accumulation and transcription factors. However, the capability of single gene transformation is limited. Multiple gene transfer might be important to solve the problem. Both hydrolysis and solubilization are important for the efficient mobilization of Po. To improve the efficiency of organic acids, modifications of both synthesis and transport might be necessary. Systems for modulation of gene expression by promoter selection are also important for future applications. It is necessary that further research yields progress in elucidating the signaling networks for P adaptation strategies through hormonal participation, posttranscriptional regulation, and lncRNA and miRNA involvement. Furthermore, comprehension of the mechanisms of plant–microbe interaction is a persistent issue. The contribution to P accumulation by mycorrhizal fungal symbiosis is extremely important in natural conditions. Root-secreted strigolactones play an important role in infection by arbuscular mycorrhizal fungi via induction of hyphae branching.132 Regulation of strigolactone synthesis and exudation might contribute to AMF-dependent P accumulation in natural soil conditions. Other various plant–microbe interactions also exist. They are known as important processes for P acquisition and competition. Progress in the complex phenomena involved in P dynamics in the rhizosphere soils must be applied to the function of P mobilization to achieve sustainable agriculture through the efficient use of sparingly available P and limited P resources.

ACKNOWLEDGMENTS This research was supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan through Grants-in-Aid (20053015 and 23688010), and as part of Joint Research Program implemented at the Institute of Plant Science and Resources, Okayama University in Japan, and Grant-in-Aid for Fundamental Research by Graduate School of Biosphere Science, Hiroshima University in Japan.

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98. Sun F, Suen PK, Zhang Y, et al. A dual-targeted purple acid phosphatase in Arabidopsis thaliana moderates carbon metabolism and its overexpression leads to faster plant growth and higher seed yield. New Phytol 2012;194:206–19. 99. Zhang Y, Yu L, Yung KF, et al. Over-expression of AtPAP2 in Camelina sativa leads to faster plant growth and higher seed yield. Biotechnol Biofuel 2012;5:19. 100. Zhang Y, Sun F, Fettke J, et al. Heterologous expression of AtPAP2 in transgenic potato influences carbon metabolism and tuber development. FEBS Lett 2014;588:3726–31. 101. Nakamura Y, Koizumi R, Shui G, et al. Arabidopsis lipins mediate eukaryotic pathway of lipid metabolism and cope critically with phosphate starvation. Proc Natl Acad Sci 2009;106:20978–83. 102. Lambers H, Cawthray GR, Giavalisco P, et al. Proteaceae from severely phosphorus-impoverished soils extensively replace phospholipids with galactolipids and sulfolipids during leaf development to achieve a high photosynthetic phosphorus-use-efficiency. New Phytol 2012;196:1098–108. 103. Sulpice R, Ishihara H, Schlereth A, et al. Low levels of ribosomal RNA partly account for the very high photosynthetic phosphorus-use efficiency of Proteaceae species. Plant Cell Environ 2014;37:1276–98. 104. Rubio V, Linhares F, Solano R, et al. A conserved MYB transcription factor involved in phosphate starvation signaling both in vascular plants and in unicellular algae. Genes Dev 2001;15:2122–33. 105. Yi K, Wu Z, Zhou J, et al. OsPTF, a novel transcription factor involved in tolerance to phosphate starvation in rice. Plant Physiol 2005;138:2087–96. 106. Devaiah BN, Karthikeyan AS, Raghothama KG. WRKY75 transcription factor is a modulator of phosphate acquisition and root development in Arabidopsis. Plant Physiol 2007;143:1789–801. 107. Devaiah BN, Madhuvanthi R, Karthikeyan AS, et al. Phosphate starvation responses and gibberellic acid biosynthesis are regulated by the MYB62 transcription factor in Arabidopsis. Mol Plant 2009;2:43–58. 108. Bustos R, Castrillo G, Linhares F, et al. A central regulatory system largely controls transcriptional activation and repression responses to phosphate starvation in Arabidopsis. PLoS Genet 2010;6:e1001102. 109. Dai X, Wang Y, Zhang WH. OsWRKY74, a WRKY transcription factor, modulates tolerance to phosphate starvation in rice. J Exp Bot 2016;67:947–60. 110. Fang W, Ding W, Zhao X, et al. Expression profile and function characterization of the MYB type transcription factor genes in wheat (Triticum aestivum L.) under phosphorus deprivation. Acta Physiol Plant 2016;38:5. 111. Shi S, Zhang F, Xiao K. Expression pattern and function analyses of the MADS transcription factor genes in wheat (Triticum aestivum L.) under phosphorus starvation condition. J Integr Agric 2016;15:1703–15. 112. Bari R, Pant BD, Stitt M, Scheible W-R. PHO2, MicroRNA399, and PHR1 define a phosphate-signaling pathway in plants. Plant Physiol 2006;141:988–99. 113. Chiou TJ, Aung K, Lin SI, et al. Regulation of phosphate homeostasis by microRNA in Arabidopsis. Plant Cell 2006;18:412–21. 114. Chiou TJ, Lin SI. Signaling network in sensing phosphate availability in plants. Ann Rev Plant Biol 2011;62:185–206. 115. Lei L, Li Y, Wang Q, et al. Activation of MKK9-MPK3/MPK6 enhances phosphate acquisition in Arabidopsis thaliana. New Phytol 2014;203:1146–60. 116. Wu P, Shou H, Xu G, et al. Improvement of phosphorus efficiency in rice on the basis of understanding phosphate signaling and homeostasis. Curr Opin Plant Biol 2013;16:205–12. 117. Guo M, Ruan W, Li C, et al. Integrative comparison of the role of the PHOSPHATE RESPONSE1 subfamily in phosphate signaling and homeostasis in rice. Plant Physiol 2015;168:1762–76. 118. Klecker M, Gasch P, Peisker H, et al. A shoot-specific hypoxic response of Arabidopsis sheds light on the role of the phosphate-responsive transcription factor PHOSPHATE STARVATION RESPONSE1. Plant Physiol 2014;165:774–90. 119. Wang J, Sun J, Miao J, et al. A phosphate starvation response regulator Ta-PHR1 is involved in phosphate signalling and increases grain yield in wheat. Ann Bot 2013;111:1139–53.

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120. Wu P, Xu J. Does OsPHR2, central Pi-signaling regulator, regulate some unknown factors crucial for plant growth? Plant Signal Behav 2010;5:712–4. 121. Zhou J, Jiao FC, Wu Z, et al. OsPHR2 is involved in phosphate-starvation signaling and excessive phosphate accumulation in shoots of plants. Plant Physiol 2010;146:1673–86. 122. Puga MI, Mateos I, Charukesi R, et al. SPX1 is a phosphate-dependent inhibitor of PHOSPHATE STARVATION RESPONSE 1 in Arabidopsis. Proc Natl Acad Sci 2014;111:14947–52. 123. Yang T, Hao L, Yao S, et al. TabHLH1, a bHLH-type transcription factor gene in wheat, improves plant tolerance to Pi and N deprivation via regulation of nutrient transporter gene transcription and ROS homeostasis. Plant Physiol Biochem 2016;104:99–113. 124. Fujii H, Chiou TJ, Lin SI, et al. A miRNA involved in phosphate-starvation response in Arabidopsis. Curr Biol 2005;15:2038–43. 125. Aung K, Lin SI, Wu CC, et al. pho2, a phosphate overaccumulator, is caused by a nonsense mutation in a microRNA399 target gene. Plant Physiol 2006;141:1000–11. 126. Park BS, Seo JS, Chua NH. NITROGEN LIMITATION ADAPTATION recruits PHOSPHATE2 to target the phosphate transporter PT2 for degradation during the regulation of Arabidopsis phosphate homeostasis. Plant Cell 2014;26:454–64. 127. Wasaki J, Yonetani R, Shinano T, et al. Expression of the OsPI1 gene, cloned from rice roots using cDNA microarray, rapidly responds to phosphorus status. New Phytol 2003;158:239–48. 128. Franco-Zorrilla JM, Valli A, Todesco M, et al. Target mimicry provides a new mechanism for regulation of microRNA activity. Nat Genet 2007;39:1033–7. 129. Janboune M, Secco D, Lecampion C, et al. A rice cis-natural antisense RNA acts as a translational enhancer for its cognate mRNA and contributes to phosphate homeostasis and plant fitness. Plant Cell 2013;25: 4166–82. 130. Wang L, Zengj HQ, Song J, et al. miRNA778 and SUVH6 are involved in phosphate homeostasis in Arabidpsis. Plant Sci 2015;238:273–85. 131. Lei KJ, Lin YM, Ren J, et al. Modulation of the phosphate-deficient responses by microRNA156 and its targeted SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 3 in Arabidopsis. Plant Cell Physiol 2016;57:192–203. 132. Akiyama K, Matsuzaki K, Hayashi H. Plant sesquiterpenes induce hyphal branching in arbuscular mycorrhizal fungi. Nature 2005;435:824–7.

CHAPTER

TRANSGENIC APPROACHES FOR IMPROVING NITROGEN AND POTASSIUM USE EFFICIENCY IN PLANTS

19 Xue He, Wan Teng, Yiping Tong

Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

INTRODUCTION In modern agriculture, increase in crop yield is largely dependent on fertilizers. In sustainable agricultural systems that are more economically efficient and environmentally friendly, it is desirable to use less fertilizer, yet maximize crop yield.1–3 To achieve this goal, systematic approaches are required, including optimizing management practices and breeding crops with improved nutrient use efficiency.2,4–6 Conventional breeding has greatly increased crop productivity, and the requirement for macronutrients has also increased in parallel with the yield increase. Ortiz-Monasterio et al.7 investigated the grain yield and nitrogen (N) use efficiency (NUE) in the representative wheat (Triticum aestivum) varieties produced by CIMMYT, the International Maize and Wheat Improvement Center, and released from 1950 to 1985 under four N application rates in a 3-year field study in Mexico. They found that the genetic gains in both grain yield and NUE during the period from 1950 to 1985 were 1.1, 1.0, 1.2, and 1.9% per year with application rates of 0, 75, 150, and 300 kg N ha–1, respectively. Progress in NUE resulted in an improvement of both N uptake efficiency and utilization efficiency. A 2-year field experiment was conducted with 32 Chinese wheat varieties released from 1950 to 2005 under two N application rates. The study found that grain yield, harvest index (HI), N uptake, and N accumulation in grains increased with variety development from the 1950s to the 2000s.8 The positive effects of breeding on NUE have also been demonstrated in spring barley (Hordeum vulgare),9 maize (Zea mays),10,11 and rice (Oryza sativa).12 However, modern varieties still exhibited large variation in nutrient use efficiency. For example, modern rice varieties had larger difference in grain yield; uptake of N, phosphorus (P), and potassium (K) in controls and in the presence of NPK fertilizers; as well as yield response to fertilizer application.13 As such, genetic improvement in nutrient use efficiency is required in modern varieties to ensure more economical and friendly use of fertilizers. Nutrient use efficiency is a combination of nutrient uptake efficiency (acquisition of nutrients from soil) and nutrient utilization efficiency (higher dry matter production per unit of nutrients taken up). NUE is governed by complicated gene networks that mediate the uptake, assimilation, remobilization, and storage of N. Currently, many candidate genes have been identified for improvement of NUE in crop plants, and these candidate genes exist in pathways related to uptake, assimilation, remobilization, and storage of N.5,14–17 As the use of N assimilation genes in engineering N-efficient plants has been Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00019-3 Copyright © 2017 Elsevier Inc. All rights reserved.

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reviewed in several recent publications,5,16–18 this chapter will focus on manipulating NUE by employing the genes that regulate root growth, N transport, and leaf functional duration. K use efficiency (KUE) is also regulated by a complex gene network associated with uptake and utilization efficiency. K uptake largely depends on root morphology and distribution in soils, as well as net K+ influx in roots. The mechanisms underlying utilization efficiency include the translocation of K into different organs, the capacity to maintain cytosolic K+ concentration within optimal ranges, and the increased capacity to substitute Na+ for K+.19,20 To date, most advances in engineering KUE have been achieved by manipulating the expression of K+ channels and transporters.

ENGINEERING NUE IMPROVING NUE BY ENGINEERING ROOT GROWTH Plant roots are the main site for uptake of nutrients from soil, and their size and distribution affect the acquisition of soil nutrient resources by plants. Root morphology and its response to N availability are both regulated by complex gene networks. Many genes that regulate root growth have been shown to have potential for engineering N-efficient plants. CCAAT-binding transcription factors are conserved among all eukaryotes; they are named NF-Y in plants, CBF in mammals, and Hap in yeast (Schizosaccharomyces pombe).21 NF-Y transcription factors are heterotrimers composed of three subunits: NF-YA, NF-YB, and NF-YC. The HAP complex is reported to regulate the expression of glucose dehydrogenase 1 (GDH1) and an N starvation–specific gene, isp6+ in yeast.22–24 Recently, NF-Y transcription factors were also found to regulate N use in higher plants. A recent study that performed a genomewide sequence analysis of NF-Y transcription factors in wheat identified 18 NF-YAs, 34 NF-YBs, and 28 NF-YCs. The expression of most NF-YAs positively responded to low N availability. Overexpressing TaNFYA-B1, a low N– and low P–inducible NF-YA transcript factor on chromosome 6B, significantly increased both N uptake and grain yield under differing N-supply levels in a field experiment. The increased N uptake may have resulted from the fact that that overexpressing TaNFYA-B1 stimulated lateral branching, upregulated the expression of nitrate transporters NRT1.1 and NRT2.1, and increased root NO −3 influx.25 The NO −3 inducible NAC (NAM, ATAF, and CUC) transcription factor TaNAC2-5A has been found to be involved in NO −3 signaling in wheat.26 Overexpression of TaNAC2-5A in wheat enhanced root growth and NO −3 influx rate, and increased the ability of the roots to acquire N. In a field experiment, TaNAC2-5A–overexpressing transgenic wheat lines had higher grain yield and higher N accumulation in aerial parts and allocated more N in grains.26 Longer root length and more roots in deeper layers of soil have been proposed to increase nutrient acquisition in the deep soil layers. DEEPER ROOTING 1 (DRO1), a rice quantitative trait locus (QTL) that controls the root growth angle, has recently been cloned. DRO1 functions downstream of the auxin signaling pathway and controls cell elongation in the root tip, which causes asymmetric root growth and downward bending of the root in response to gravity.27 Overexpression of DRO1 increased the root growth angle, which led the roots to grow in a more downward direction.27 Arai-Sanoh et al.28 compared the yield and NUE of the shallow-rooting IR64 and the deep-rooting Dro1-NIL (a near-isogenic line homozygous for the functional DRO1 in the IR64 genetic background with a nonfunctional allele of DRO1) in an irrigated paddy field, with and without N fertilizer. They found that Dro1-NIL showed approximately 10% higher grain yield than IR64, irrespective of the fertilizer treatment. Detailed analysis showed that the uptake of N from soil and leaf N concentration were higher in Dro1-NIL than in IR64 after heading.28

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In plants, the plasticity of root architecture in response to N availability largely determines N acquisition efficiency.29,30 A well-known example is that plants modify root system architecture through stimulating lateral root elongation in locally concentrated NO −3 patches. This localized stimulatory effect has been shown to be a direct effect of the NO −3 itself rather than a nutritional effect, and requires the functions of the MADS-box gene AtANR1 in Arabidopsis.31 OsMADS25 is one of the five AtANR1-like genes in rice and belongs to the AtANR1 clade. OsMADS25 overexpression in transgenic rice resulted in an increase in primary root length, lateral root number, lateral root length, and shoot fresh weight in the presence of NO −3 . Furthermore, overexpressing of OsMADS25 promoted NO −3 accumulation and significantly increased the expressions of nitrate transporter genes at high rates of NO −3 supply.32 OsTOND1 corresponding to the major QTL for tolerance to N deficiency in the indica cultivar Teqing encodes a thaumatin protein.33 OsTOND1 seemed to regulate low N–induced primary root elongation. Overexpressing OsTOND1 in rice increased shoot dry weight, shoot N concentration, N uptake, and primary root length in comparison with the control plants under N-deficient hydroponic conditions, and increased panicles number, grain number per plant, and grain yield per plant under N-deficient field conditions.33 In addition, overexpressing OsTOND1 increased the expression of several genes that encode N assimilation enzymes.33 As OsTOND1 is present only in some of the indica cultivars and is absent in all the japonica cultivars examined,33 manipulating OsTOND1 expression provides a promising approach in breeding rice with improved yield and NUE lateral root branching.33 It is well known that low N availability can stimulate lateral root branching.29,30 A recent study showed that low N–induced lateral root number increases depends on the function of the auxin biosynthesis gene tryptophan aminotransferase related 2 (AtTAR2) in Arabidopsis.34 Overexpression of AtTAR2 increases lateral root number and total lateral root length under both high- and low-N conditions,34 suggesting a new strategy for improving NUE through the engineering of TAR2 expression in roots.

IMPROVING NUE BY MANIPULATION OF N TRANSPORTERS N is most often taken up by plants as water-soluble NO −3 and NH +4 , and to a lesser degree as proteins, peptides, or amino acids.35–38 In soil solutions, there are heterogeneity and dynamic variations of NO −3 and NH +4 concentrations, which range from lower than 100 µM to higher than 10 mM,38 so plant roots have uptake systems for both NO −3 and NH +4 with different affinities. In higher plants, there are two types of nitrate transporters, known as NRT1/PTR family (NPFs) and nitrate transporter 2 family (NRT2s). While most NPFs are low-affinity nitrate transporters, an exception is AtNPF6.3 (known as NRT1.1/CHL1), which operates over both ranges by phosphorylation and dephosphorylation.39,40 Some members of NPFs are nitrate transporters, while others are peptide transporters.41,42 NRT2s are highaffinity nitrate transporters; some NRT2 members require a partner protein, NAR2 (NRT3), for NO −3 transport at relatively low concentration ranges.43,44 The expression levels of the N transporters have been found to correlate with N uptake in crops. In Brazil, the landrace rice varieties traditionally cultivated by low-input farmers displayed high NUE compared with the improved varieties.45 A study on the effect of NO −3 levels on NO −3 uptake kinetics in the landrace rice variety Piauí and the improved rice variety IAC-47 showed that Piauí exhibited higher Vmax and lower Km values for NO −3 than IAC-47 when cultivated at low levels of NO −3 .45 Compared to IAC-47, Piauí presented a higher transcription of the high-affinity nitrate transporters OsNAR2.1, OsNRT2.1, OsNRT2.2, and OsNRT2.3a and accumulated more NO −3 in the shoots, sheath, and roots under low-NO −3 conditions.46 In rice, OsNAR2.1 interacts with OsNRT2.1, OsNRT2.2, and OsNRT2.3a

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to provide NO −3 uptake over high and low concentration ranges.43,47 OsNRT2.3 has two splice forms, OsNRT2.3a and OsNRT2.3b. OsNRT2.3a encodes a plasma membrane protein of 516 amino acids, whereas OsNRT2.3b encodes a shorter 486–amino acid plasma membrane protein expressed moderately in the phloem of the shoot and faintly in the root.47,48 However, the affinity of OsNRT2.3a and OsNRT2.3b for NO −3 showed similar Km values at approximately 0.45 mM, when the membrane potential exceeded −100 mV, the normal level for plant cells.49 A comparison of the expression ratio of OsNRT2.3b and OsNRT2.3a in the shoots of 10 indica rice varieties revealed a strong correlation between the expression ratio and N content in shoots under low N supply, but this correlation was missing under normal N supply.49 In field experiments with N fertilizer application rates ranging from 0 to 300 kg N ha–1, overexpression of OsNRT2.3b in rice improved grain yield and NUE (grain yield/ applied N fertilizer) by 40%.49 Although OsNRT2.1 needs OsNAR2.1 for NO −3 transport activity, overexpression of OsNRT2.1 alone greatly increased yield and NUE in rice, but the effects depended on the promoter used to drive OsNRT2.1 expression. When the maize ubiquitin promoter (pUbi) and the NO −3 -inducible promoter of the OsNAR2.1 gene were used to drive OsNRT2.1 expression in transgenic rice plants, the pUbi::OsNRT2.1 transgenic lines of T4 generation had 17% lower grain yield and 14% higher biomass yield than those of the wild type, and the pOsNAR2.1::OsNRT2.1 transgenic lines had 25% higher grain yield and 27% higher biomass yield than those of the wild type at the level of 180 kg N ha–1. At the level of 300 kg N ha–1, the grain yield of the pUbi::OsNRT2.1 transgenic lines was reduced by 16%, and the biomass yield increased by 12%. As for the pOsNAR2.1::OsNRT2.1 transgenic lines, the grain yield and biomass yield were increased by 21% and 22%, respectively, compared with those of the wild-type.50 These results suggested that overexpression of OsNRT2.1 by using a constitutive strong promoter inhibited dry matter translocation to grains. Overexpression of OsNRT2.1 under the control of the pUbi promoter also inhibited N translocation to grain, as N harvest index (NHI) of the pUbi::OsNRT2.1 transgenic lines was reduced to approximately 71% of the wild-type values; however, the translocations of dry matter and N to grains were not significantly affected by expressing pOsNAR2.1::OsNRT2.1.50 Compared to the wild type, the NUE of the pOsNAR2.1::OsNRT2.1 lines was greatly increased, as evidenced by the increases in total N accumulation at anthesis and maturity, agronomic NUE [(grain yield − grain yield of zero N plot)/N supply], and N recovery efficiency [(total N accumulation at maturity for N-treated plot – total N accumulation at maturity of zero N plot)/N supply].50 Taken together, efficient high-affinity nitrate transporter systems can increase the adaptation of rice plants to low N availability in the soil, and also improve yield and NUE under a wide range of N supply levels. To achieve these goals, however, a suitable promoter is required to manipulate the expression of high-affinity nitrate transporters. Asian cultivated rice consists of two main subspecies, indica and japonica. The indica subspecies has higher NO −3 -absorption activity than the japonica subspecies. A recent study found that that the variation in a nitrate transporter gene of the NPF family, OsNRT1.1B (OsNPF6.5), may contribute to this divergence in NO −3 use.51 In comparison with the variation of OsNRT1.1B from japonica subspecies, OsNRT1.1B indica variation was associated with higher NO −3 uptake activity, root-to-shoot NO −3 transport, and expression of NO −3 -responsive genes. Field experiments with near-isogenic and transgenic lines demonstrated that the japonica variety carrying the OsNRT1.1B indica allele had significantly higher grain yield and NUE compared to the variety without that allele.51 Several NPF genes encoding peptide transporter also show potential in improving NUE. AtNPF8.2/AtPTR5 from Arabidopsis mediates high-affinity transport of dipeptides,52 and overexpression of AtNPF8.2/AtPTR5 resulted in enhanced shoot growth and increased N content.53 In rice, overexpression of OsPTR6 increased plant

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height, biomass, plant N accumulation, and glutamine synthetase (GS) activities at 5.0-mM NH +4 and 2.5-mM NH4NO3 in comparison with the nontransgenic wild-type control. The increased plant N accumulation by overexpressing OsPTR6 was possibly associated with the upregulation of OsAMT1.1, OsATM1.2, and OsAMT1.3.54 The rice OsPTR9 expression was regulated by exogenous N and by the day–night cycle. Elevated expression of OsPTR9 in transgenic rice plants enhanced NH +4 uptake, promoted lateral root branching, and increased grain yield, whereas downregulation of OsPTR9 in a TDNA insertion line and in OsPTR9-RNAi rice plants had the opposite effect.55 These studies on NPFs clearly showed that the nitrate transporter and peptide transporter of NPF family are promising genetic resources for the improvement of NUE in crops. The ammonium transporter has been successfully used to engineer NUE in rice, which uses NH +4 as the major N resource in paddy fields. OsAMT1.1 from rice encodes a plasma membrane protein, and its expression is subjected to circadian rhythm regulation.56 Overexpression of OsAMT1.1 did not change root structure, but significantly increased NH +4 uptake rate, NH +4 content in shoots and roots, and promoted expression of the genes in N assimilation and grain yield under suboptimal and optimal N conditions.56 However, the effects of overexposing OsAMT1.1 may depend on the rice genotype. When OsAMT1.1 was overexpressed in the rice varieties Jarrah and Taipei, NH +4 influx was increased in the transgenic lines of Jarrah when grown at 10-µM external NH +4 concentration, but not in the overexpression lines of Taipei.57 At 2-mM external NH +4 concentration, the Jarrah transgenic lines displayed an increased NH +4 influx, whereas the Taipei transgenic lines showed reduced NH +4 influx rates.57 These results suggested that overexpressing OsAMT1.1 exerts distinct effects on NH +4 influx in Jarrah and Taipei. In contrast with the positive effects reported by Ranathunge et al.,56 overexposing OsAMT1.1 in Jarrah and Taipei inhibited or did not significantly affect plant growth when grown at low and high external NH +4 concentrations.57

IMPROVING NUE BY MANIPULATION OF TRANSCRIPTION FACTORS A number of transcription factors in the N-signaling network have been identified; these are known to modulate the expression of genes involved root development, N transport and assimilation, remobilization, and storage of N.58–60 Some of these transcription factors have been shown to be valuable in engineering crops with improved NUE. It has been mentioned earlier that overexpressing the CCAAT-binding transcription factor TaNFYAB1 in wheat stimulated lateral branching, upregulated the expression of nitrate transporters TaNRT1.1 and TaNRT 2.1, and increased root NO −3 influx, consequently increasing N uptake and grain yield under differing N-supply levels.25 The NO −3 -inducible NAC transcription factor TaNAC2-5A directly regulates the genes mediating N transport and assimilation.26 Overexpression of TaNAC2-5A in wheat enhanced root growth, NO −3 influx rate, N accumulation in aerial parts, NHI, grain N concentration, and grain yield under both low- and high-N conditions.26 NO −3 is both a key nutrient and a signaling molecule for plants. In addition to TaNAC2-5A in wheat, several transcription factors (ANR1, NLP7, LBD37/38/39, SPL9, and TGA1/TGA4) that regulate gene expression in response toNO −3 have been identified in Arabidopsis.31,61–64 It has been mentioned earlier that the ANR1-like transcription factor OsMADS25 from rice has been used to improve plant growth and NO −3 uptake.32 NIN-LIKE PROTEIN 7 (NLP7) is another potential candidate for improving plant N use ability. AtNLP7 plays a key role in NO −3 signaling by binding the NO −3 -responsive cis-element and activating NO −3 -responsive cis-element–dependent and NO −3 -responsive transcription. Also, the suppression of AtNLP7 function impairs the NO −3 -inducible

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expression of a number of genes and causes severe growth inhibition.61,64 Overexpressing AtNLP7 in Arabidopsis increased plant biomass under both low- and high-N conditions with a better-developed root system and a reduced shoot/root ratio. Overexpressing AtNLP7 in tobacco (Nicotiana tabaccum) also improved plant growth and N use.65 More detailed analysis revealed that overexpressing AtNLP7 in Arabidopsis showed a significant increase in key N metabolites, N uptake, total N content, and expression levels of genes involved in N assimilation and signaling. Moreover, overexpression of AtNLP7 also enhanced the photosynthesis rate and carbon assimilation.65 Considering that carbon skeletons synthesized in photosynthesis are required for the assimilation of N, efficient carbon flow is therefore important in promoting N assimilation. This idea is further supported by the positive effects of the plant-specific transcription factor DNA BINDING WITH ONE FINGER (Dof1) on NUE. ZmDOF1 from maize is an activator for multiple gene expression associated with organic acid metabolism, such as the phosphoenolpyruvate carboxylase (PEPC) gene.66,67 Overexpression of ZmDof1 in Arabidopsis increased the expression of PEPC and several genes involved in the tricarboxylic acid cycle, thereby producing more carbon skeletons for the assimilation of N.68 This transcription factor also has been used successfully to increase the net photosynthesis rate and carbon flow toward N assimilation, and improve N assimilation and growth of rice under low-N conditions.69 A recent report suggested that the bZIP transcription factor ELONGATED HYPOCOTYL5 (AtHY5) mediates light-regulated coupling of shoot growth and carbon assimilation with root growth and N uptake.70 AtHY5 was found to be a shoot-to-root mobile signal; AtHY5 in shoots promoted carbon assimilation and translocation, whereas in roots, AtHY5 activated AtNRT2.1 expression and potentiated NO −3 uptake by increasing carbon photoassimilate (sucrose) levels.70 This finding shows the potential of HY5 in improving NUE in crops. Recently, a major QTL (DEP1, DENSE AND ERECT PANICLES 1) for panicle architecture was found to control NUE in rice. The dominant allele at the DEP1 locus (dep1-1) is a gain-of-function mutation and can increase grain number per panicle and consequently, grain yield.71 The NIL carrying the dep1-1 allele had increased transcript levels of key genes associated with NH +4 uptake and assimilation (i.e., OsAMT1.1, OsGS1.2, and OsNADH-GOGAT1), as well as N uptake and grain yield at moderate levels of N fertilization, compared to the NIL with the DEP1 allele.72 The DEP1 protein interacts in vivo with both the G-protein α-subunit (RGA1) and the G-protein β-subunit (RGB1), and reduced RGA1 or enhanced RGB1 activity, inhibiting N responses.72 As such, G-protein pathways are valuable in improving grain yield and NUE.

IMPROVING NUE BY INCREASING POSTANTHESIS N UPTAKE AND DELAYING SENESCENCE Grain filling is critical in determining yield and nutrient storage in grains. A study on a historical series of wheat varieties commercially released in Australia revealed that the higher grain yield of modern wheat varieties was achieved with a high relative growth rate during the vegetative phase and a greater crop growth rate from ear emergence to harvest.73 Indeed, by using tall and semidwarf isogenic lines of spring wheat, Miralles and Slafer74 found that the semidwarf line has higher biomass accumulation and radiation use efficiency after anthesis compared with the tall line. Modern wheat varieties with higher yield can also accumulate higher dry matter than those with lower yield.75,76 An analysis of the time course of dry matter accumulation in modern wheat varieties by the collection of 413 data points from 11 field experiments in China demonstrated a positive correlation between grain yield and postanthesis

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dry matter accumulation.77 This is also true in other cereals. In modern rice varieties, the high-yield rice varieties tend to accumulate higher dry matter and more N, P, and K after flowering than the low-yield rice varieties.13 It has been reported that N-efficient maize hybrids were characterized by a higher–dry matter production during grain filling under limited N supply compared with inefficient hybrids.77–79 The capacity of dry matter accumulation during grain filling largely depends on leaf functional duration.79–83 Indeed, prolonged leaf functional duration has contributed to the increased yield in most of the major crops.84 A summary of the analysis on the contribution of physiological traits to yield increase revealed that plant breeding almost doubled the grain yield of cereals in the last century by increasing leaf area, daily duration of photosynthesis, or leaf area duration; surprisingly, plant breeding did not significantly increase the rate of photosynthesis per unit leaf area.83–85 The maintenance of green leaf area and the onset of senescence are genetically controlled and are also highly dependent on environmental conditions, including soil N availability. The longevity and photosynthetic capacity of a leaf are related to its N status. It is well known that low N availability severely reduces crop yield with a lower leaf N content, a smaller green leaf area, and hence also an accelerated senescence process, while N fertilizer application has the opposite effect.85,86 Increased N fertilizer has been proposed to be a quick and relatively inexpensive substitute for genetic increases in leaf area, leaf area duration, and leaf N content.85 During grain filling, the remobilization of N stored in vegetative tissues causes N losses in leaves, and rapid N losses in leaves may cause early leaf senescence. A wheat variety with improved NUE should have efficient postanthesis remobilization of N from the stem to grain, but less efficient remobilization of N from leaves to grain to prevent early senescence of leaves.87 As such, maintenance of root activity in acquiring N from soil after anthesis is therefore important in overcoming the tradeoff between N remobilization and senescence.79,81,85,88 There are substantial genotypic differences in postanthesis N uptake in wheat,88–90 rice,13 maize,77–79 and sorghum (Sorghum bicolor).79 Many studies supported that increasing postanthesis N uptake can prolong leaf functional duration and improve grain productivity in wheat,76,81,88,90,91 rice,13 maize,78,92 and sorghum.79 As such, increasing postanthesis N uptake and leaf functional duration is important in improving NUE. The studies on maize by Worku et al.78,92 support this idea. These studies found that the increased yield of the N-efficient maize hybrids under N stress was associated with higher postanthesis N uptake, grain production per unit N accumulation, and NHI. The N-efficient maize hybrids also maintained more green leaves, had lower leaf senescence, and had higher leaf chlorophyll content during and after flowering compared with the inefficient hybrids.78,92 Increasing postanthesis N uptake would also be an effective approach for improving grain yield and grain protein concentration (GPC) simultaneously in wheat. GPC is a major determinant of wheat end-use value and baking quality, and an N-efficient wheat ideotype has been suggested to have high grain mass, large HI and NHI, and high GPC.18 However, the negative correlation between grain yield and GPC makes it difficult to increase these two traits simultaneously. An earlier study showed that postanthesis N uptake was positively correlated with grain yield and GPC in wheat.93 More recently postanthesis N uptake has been found to highly correlate with grain protein deviation (the difference from the value predicted by yield).94,95 Therefore, improving postanthesis N uptake provides a way to shift the negative correlation between grain yield and GPC in wheat. Many studies have supported the importance of maintaining root growth in efficient postanthesis N uptake and delaying senescence during grain filling. N-efficient maize varieties were characterized by maintenance of N-uptake activity during the reproductive growth phase through root growth, leaf area duration, and photosynthetic activity of leaves.78,96 The root growth and distribution in the soil profile

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are also important for efficient N uptake and delaying senescence after flowering. A recent study found that the maize varieties with early senescing and stay-green leaves had similar total root length and vertical root distribution in the 0–60 cm soil profile at silking. However, the stay-green maize varieties had longer postsilking root length and more roots in deeper soil layers than the early-senescing ones at maturity.97 Longer root length and more roots in deeper soil layers have been proposed to increase nutrient acquisition in the deep soil layer. DRO1, which controls root growth angle, has been shown to improve the uptake of N from soil and leaf N concentration after heading.28 The genes involved in N transport and assimilation are found to be critical in efficient postanthesis N uptake and delaying senescence in cereals. It has been shown that during leaf senescence, the plastidic GS isoenzyme (GS2) experiences rapid loss and the cytosolic isoenzyme GS1 becomes the major GS isoform in wheat and maize.98,99 Martin et al.99 found that the late-senescing maize hybrid Nicco had higher chlorophyll content, free amino acid content, and higher GS2 protein abundance in leaves than the early-senescing maize hybrid Tarro after silking. In wheat, a recent study showed that postNO −3 uptake correlated well with the expression of the nitrate transporter TaNRT2.1 in roots, suggesting a major role of TaNRT2.1 in postanthesis NO −3 uptake in wheat.100 It has been reported that transgenic expression of OsNRT2.1 under the promoter of OsNAR2.1 increased postanthesis N uptake, dry matter accumulation, and grain yield in rice.50 Postanthesis N uptake is also tightly correlated to nitrate reductase activity in wheat.89,90 These findings enable the improvement of postanthesis N uptake by the manipulation N assimilation and transport activities. A number of transcription factors from different families have been identified to regulate senescence and show potential in manipulating N use, senescence, and productivity of crop plants. A NAC transcription factor (NAM-B1) has been found to accelerate leaf senescence and increase N remobilization from leaves to developing grains in wheat.101 Reduction in RNA levels of the multiple NAM homologs by RNA interference (RNAi) delayed senescence by more than 3 weeks and reduced wheat grain protein, zinc, and iron contents by more than 30%.101 The NAC transcription factor TaNAC-S from wheat showed decreased transcript abundance during postanthesis leaf senescence. Overexpressing TaNAC-S resulted in delayed leaf senescence and higher grain N concentration at similar grain yields compared to nontransgenic controls, possibly by enhancing postanthesis N uptake.102 In rice, the NAC transcription factor OsNAP was found to positively regulate leaf senescence and accumulation of N and other mineral nutrients in grains. Reducing OsNAP expression by RNAi led to delayed leaf senescence and an extended grain-filling period, as well as increases in seed-setting rate, 1000-grain weight, and grain yield.103 However, the potential of these transcription factors in improving crop productivity under low-N conditions needs further investigation. Cytokinin is the critical phytohormone that regulates plant growth in relation to N availability and also the senescence process. Cytokinin homeostasis is maintained through biosynthesis, activation, inactivation, reactivation, and degradation.104–106 The isopentenyl transferase (IPT) gene encodes IPT, an enzyme that catalyzes the rate-limiting step in cytokinin biosynthesis. An increase in the expression of the IPT gene can retard the senescence process and the remobilization of leaf proteins.107,108 A large amount of studies have shown that autoregulated expression of the IPT gene under the control of the senescence- or development-regulated promoters delayed senescence in a variety of dicots and cereals (for review, see Ref. [86]). The promoter of the SAG12 gene from Arabidopsis (PSAG12) is known to have a strictly senescence-specific pattern of expression.109 Expression of PSAG12::IPT in tobacco successfully delayed senescence and improved biomass and yield.107,110 The PSAG12::IPT transgenic rice plants had a prolonged leaf photosynthetic duration and a higher seed-setting rate and panicle number

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per plant than the nontransgenic control plants.111 The transgenic plants with autoregulated expression of the IPT gene also exhibited delayed leaf senescence and enhanced plant productivity as in other plants, such as Arabidopsis,112,113 cotton (Gossypium hirsutum),114 peanut (Arachis hypogaea),115 creeping bentgrass (Agrostis stolonifera),116 and perennial grass species (A. stolonifera).117 Autoregulated expression of the IPT gene delays leaf senescence under low-N conditions in maize,118 tobacco,107,119 lettuce (Lactuca sativa),120 and wheat,121 and shows potential in improving NUE. Expressing the IPT gene in tobacco under the control of the promoter from a senescence-associated receptor protein kinase (PSARK) increased N uptake flux rate, N accumulation in leaves, and leaf dry weigh under sufficient and limited N conditions.119,122 Expressing PSAG12::IPT in wheat delayed leaf senescence and increased NO −3 influx before and after flowering. However, the PSAG12::IPT transgenic and the wild-type plants did not show differences in yield-related parameters, including the number of grains and grain weight; this is possible because the delay of leaf senescence also delays the translocation of metabolites from leaves to developing grains.121 It should be mentioned here that the positive effects of delaying leaf senescence by autoregulated expression of the IPT gene on productivity and NUE depend on the plant species, the productivity parameter, and the environment. Besides the IPT gene involved in cytokinin biosynthesis, cytokinin oxidase/dehydrogenase (CKX), which mediates the degradation of cytokinins, is another candidate for manipulating cytokinin level and hence leaf senescence processes. In naturally occurring rice cultivars, a QTL for increased grain number has been identified to encode a CKX, OsCKX2. In the loss-of-function Osckx2 mutant, increased cytokinin accumulation in the inflorescence meristem was associated with a yield increase of >20%.123 A reduction in CKX activity also can enhance the level of cytokinin in the shoot apical meristem and lead to change in inflorescence complexity, potentially leading to increased seed number and possibly yield.124 A recent study showed that moderately enhancing the cytokinin level by downregulation of GhCKX expression in cotton delayed leaf senescence and increased fiber and seed yield.125 However, whether downregulation of CKX expression in crops improves NUE needs to be studied in the future.

ENGINEERING KUE The K uptake and redistribution of K+ in different organs and cellular K+ homeostasis are mediated by a large number of genes that encode K+ transporters and channels in plants. The K+ channel proteins in higher plants are encoded by the genes from three families, the Shaker, tandem-pore K+ (TPK), and K+ inward rectifier (Kir)–like family. The identified K+ transporters are mainly from the K+ transporter/ high-affinity K+ transporter/K+ uptake permease (KUP/HAK/KT), high-affinity K+ transporter (HKT), Na+/H+ antiporter (NHX), and cation:proton antiporter (CHX) families.126

MODIFICATION OF K CHANNELS FOR HIGHER K UPTAKE AND USE The inward-rectifier K+ channel AtAKT1 plays a crucial role in K+ acquisition by Arabidopsis roots. Compared to the wild type, the akt1 mutant displayed a reduced tissue K+ content and is sensitive to low-K+ stress.127 AtAKT1 is subjected mainly to posttranslational regulation. AtAKT1 is phosphorylated and activated by the protein kinase AtCIPK23, which interacts with two calcineurin B– like proteins AtCBL1/AtCBL9.128 Lesion of AtCIPK23 significantly reduced K+ uptake and caused leaf chlorosis and growth inhibition, whereas overexpression of AtCIPK23 significantly enhanced K+

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uptake and tolerance to low K+ in Arabidopsis.128 Overexpression of AtCIPK23 + AtCBL1 + AtAKT1, AtCIPK23 + AtCBL9 + AtAKT1, and AtCIPK23+ AtCBL1 + AtAKT1 + AtCBL9 exhibited significantly tolerant phenotypes to low K+ and low K+ combined with low Ca2+ compared to the wild type.129 Recently, AtCIPK23 has been found to regulate the KT/HAK/KUP transporter, AtHAK5-mediated highaffinity K+ uptake in Arabidopsis roots.130 Expression in a heterologous system showed that AtCBL1, AtCBL8, AtCBL9, and AtCBL10, together with AtCIPK23, activated AtHAK5 in vivo, and this activation produced an increase in the affinity and the Vmax of K+ transport.130 As such, the positive effects of AtCIPK23 on K+ uptake and tolerance to low-K+ stress are associated with its regulation of the activities of AtAKT1 and AtHAK5. AtCIPK23 has been successfully used in engineering crops with improved KUE. Overexpression of AtCIPK23 in tobacco resulted in increased dry biomass, primary root length, K+ content, and growth status of transgenic tobacco plants compared to the wild-type controls when both were treated in lowK+ Murashige and Skoog (MS) medium and low-K+ hydroponics. The increased tolerance to low-K+ stress in the transgenic tobacco plants was conferred by increasing the K+ uptake rate under low-K+ conditions.131 Overexpression of AtCIPK23 also increased the tolerance of potato (Solanum tuberosum) to low-K+ stress.132 The Shaker-like K+ channel, GhAKT1, from cotton was predominantly expressed in cotton leaves with low abundance in roots, stem, and shoot apex. When it was overexpressed in Arabidopsis, it enhanced the growth of transgenic seedlings under a low-K+ condition and raised the net K+ influx in roots at 100-µM external K+ concentration, within the range of operation of the high-affinity K+ uptake system.133 The vacuolar TPK channel OsTPKb from rice was predominantly expressed in the tonoplast of small vacuoles. Transgenic rice plants that experienced overexpression of OsTPKb had better growth when exposed to low-K+ or water stress conditions compared to the nontransgenic controls.134 The enhanced K+ uptake of the overexpression lines may be driven by increased AtAKT1 and AtHAK1 activity, and lead to increased tissue K+ levels in roots and shoots. A more detailed assay demonstrated that overexpression of OsTPKb increased cytoplasm:vacuole K+ ratio, suggesting an important role of OsTPKb in regulating general cellular K+ homeostasis and thus stress tolerance.134

GENETIC MANIPULATION OF K TRANSPORTERS FOR IMPROVEMENT OF KUE In plants, KUP/HAK/KT is the largest K+ transporter family. In Arabidopsis, the AtHAK5 transporter mediates K+ uptake in the high-affinity range of concentrations and is required for K+ absorption, which are necessary to sustain plant growth at low K+ in the absence, as well as in the presence, of salinity.135 Recently, four transcription factors were identified that regulate the expression of AtHAK5.136 These four genes, Dwarf and Delayed Flowering 2 (DDF2), Jagged Lateral Organs (JLO), Transcription initiation Factor II_A gamma chain (TFII_A), and basic Helix-Loop-Helix 121 (bHLH121), were upregulated by low-K+ and salt stress. Overexpressing each of these four transcription factors increased root growth under low-K+ conditions in comparison with controls,136 showing their potential in enabling the adaptation of plants to K-deficiency stress. OsHAK5, a member of the KUP/HAK/KT family in rice, was expressed in various tissue organs from roots to seeds, abundantly in root epidermis and stele, the vascular tissues, and mesophyll cells, which highlight its major roles in K+ acquisition by roots at low external K+ and in K+ upward transport from roots to shoots in K+-deficient rice plants.137 Overexpression of OsHAK5 increased net K+ influx rate in roots and K+ transport from roots to aerial parts in 0.1- and 0.3-mM K+ external solution,

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increased the K+/Na+ concentration ratio in the shoots, and enhanced salt stress tolerance, while knockout of OsHAK5 had the opposite effect.137 Overexpression of the ApKUP3 gene from the aquatic plant alligator weed (Alternanthera philoxeroides) in rice resulted in a better growth performance and a strengthened K+ accumulation under different K+ supplies compared with the wild-type plants.138 Overexpression of ApKUP3 in rice also improved the tolerance to drought stress, as evidenced by a decrease in leaf water loss and increase in total leaf chlorophyll content, stomatal conductance, net photosynthetic rate, and activities of superoxide dismutase, peroxidase, and ascorbate peroxidase.138 The cation:proton antiporter, AtCHX14, was expressed in the xylem parenchyma of root and shoot vascular tissues of seedlings and was induced by elevated K+ in Arabidopsis. AtCHX14 expression in yeast and in planta mediates low-affinity K+ efflux.139 Knockout and overexpression of AtCHX14 increased and inhibited K+ uptake rate, respectively. Measurement of chlorophyll and shoot fresh weight revealed that knockout of AtCHX14 increased, while overexpressing AtCHX14 decreased the tolerance to low-K+ stress.139 In contrast to the function of AtCHX14 in mediating low-affinity K+ efflux, AtCHX13 mediates relatively high-affinity K+ uptake in plant cells with a Km of 196 µM.140 Seedlings of null chx13 mutants were sensitive to K+ deficiency conditions, whereas overexpression of AtCHX13 reduced the sensitivity to K+ deficiency by increasing K+ uptake.140 These results suggest the distinct physiological roles of CHX13 and CHX14 in controlling K+ homeostasis and K+ recirculation.

CONCLUSIONS AND FUTURE PERSPECTIVES Although NUE and KUE can be significantly improved through transgenic approaches, the genes used for generating transgenic plants are mainly associated with root growth and uptake, redistribution, and assimilation of N and K. Engineering nutrient-efficient crops not only targets the more economical and environmentally friendly use of fertilizers, but also increases yield. To date, the molecular mechanisms underlying the regulation of nutrients on agronomic important traits are still poorly understood, and this knowledge gap between nutrient use efficiency and yield responses to nutrients greatly restricts the further improvement of nutrient use efficiency and crop productivity. In recent years, genome editing with engineered nucleases has been used in plant breeding. Further identification of the genes that negatively control nutrient use efficiency and agronomic important traits will facilitate the use of genome editing in increasing nutrient use efficiency and yield.

ACKNOWLEDGMENTS This research was supported by the National Key Research and Development Program of China (2016YFD0100700) and the Keystone Project of Transfergene in China (2016ZX08002005).

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96. Horst WJ, Behrens T, Heuberger H, Kamh M, Reidenbach G, Wiesler F. Genotypic differences in nitrogen use-efficiency in crop plants. Innov Soil-Plant Syst Sust Agric Pract 2003;75–92. 97. Ning P, Li S, Li XX, Li CJ. New maize hybrids had larger and deeper post-silking root than old ones. Field Crop Res 2014;166:66–71. 98. Habash DZ, Massiah AJ, Rong HL, Wallsgrove RM, Leigh RA. The role of cytosolic glutamine synthetase in wheat. Ann Appl Biol 2001;138:83–9. 99. Martin A, Belastegui-Macadam X, Quillere I, Floriot M, Valadier MH, Pommel B, et al. Nitrogen management and senescence in two maize hybrids differing in the persistence of leaf greenness: agronomic, physiological and molecular aspects. New Phytol 2005;167:483–92. 100. Taulemesse F, Le Gouis J, Gouache D, Gibon Y, Allard V. Post-flowering nitrate uptake in wheat is controlled by N status at flowering, with a putative major role of root nitrate transporter NRT2.1. PloS One 2015;10:e0120291. 101. Uauy C, Distelfeld A, Fahima T, Blechl A, Dubcovsky J. A NAC Gene regulating senescence improves grain protein, zinc, and iron content in wheat. Science 2006;314:1298–301. 102. Zhao D, Derkx AP, Liu DC, Buchner P, Hawkesford MJ. Overexpression of a NAC transcription factor delays leaf senescence and increases grain nitrogen concentration in wheat. Plant Biol 2015;17:904–13. 103. Liang C, Wang Y, Zhu Y, Tang J, Hu B, Liu L, et al. OsNAP connects abscisic acid and leaf senescence by fine-tuning abscisic acid biosynthesis and directly targeting senescence-associated genes in rice. Proc Natl Acad Sci USA 2014;111:10013–8. 104. Werner T, Kollmer I, Bartrina I, Holst K, Schmulling T. New insights into the biology of cytokinin degradation. Plant Biol 2006;8:371–81. 105. Gajdosova S, Kaminek M, Hoyerova K, Dobrev PI, Gaudinova A, Klima P, et al. Contribution to metabolic studies of cis-zeatin—cytokinin with so far unknown function. FEBS J 2009;276:196–7. 106. Jameson PE, Song JC. Cytokinin: a key driver of seed yield. J Exp Bot 2016;67:593–606. 107. Jordi W, Schapendonk A, Davelaar E, Stoopen GM, Pot CS, De Visser R, et al. Increased cytokinin levels in transgenic PSAG12-IPT tobacco plants have large direct and indirect effects on leaf senescence, photosynthesis and N partitioning. Plant Cell Environ 2000;23:279–89. 108. Gan SS, Amasino RM. Cytokinins in plant senescence: from spray and pray to clone and play. Bioessays 1996;18:557–65. 109. Noh YS, Amasino RM. Identification of a promoter region responsible for the senescence-specific expression of SAG12. Plant Mol Biol 1999;41:181–94. 110. Boonman A, Anten NPR, Dueck TA, Jordi WJRM, van der Werf A, Voesenek LACJ, et al. Functional significance of shade-induced leaf senescence in dense canopies: an experimental test using transgenic tobacco. Am Nat 2006;168:597–607. 111. Lin YJ, Cao ML, Xu CG, Chen H, Wei J, Zhang QF. Cultivating rice with delaying led-senescence by PSAG12IPT gene transfonination. Acta Bot Sin 2002;44:1333–8. 112. Huynh LN, Van Toai T, Streeter J, Banowetz G. Regulation of flooding tolerance of SAG12:ipt Arabidopsis plants by cytokinin. J Exp Bot 2005;56:1397–407. 113. Zhang J, Van Toai T, Huynh L, Preiszner J. Development of flooding-tolerant Arabidopsis thaliana by autoregulated cytokinin production. Mol Breed 2000;6:135–44. 114. Liu YD, Yin ZJ, Yu JW, Li J, Wei HL, Han XL, et al. Improved salt tolerance and delayed leaf senescence in transgenic cotton expressing the Agrobacterium IPT gene. Biol Plant 2012;56:237–46. 115. Qin H, Gu Q, Zhang JL, Sun L, Kuppu S, Zhang YZ, et al. Regulated expression of an isopentenyltransferase gene (IPT) in peanut significantly improves drought tolerance and increases yield under field conditions. Plant Cell Physiol 2011;52:1904–14. 116. Merewitz EB, Gianfagna T, Huang BR. Protein accumulation in leaves and roots associated with improved drought tolerance in creeping bentgrass expressing an ipt gene for cytokinin synthesis. J Exp Bot 2011;62:5311–33.

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117. Xu Y, Gianfagna T, Huang BR. Proteomic changes associated with expression of a gene (ipt) controlling cytokinin synthesis for improving heat tolerance in a perennial grass species. J Exp Bot 2010;61:3273–89. 118. Robson PRH, Donnison IS, Wang K, Frame B, Pegg SE, Thomas A, et al. Leaf senescence is delayed in maize expressing the Agrobacterium IPT gene under the control of a novel maize senescence-enhanced promoter. Plant Biotechnol J 2004;2:101–12. 119. Rubio-Wilhelmi Mdel M, Sanchez-Rodriguez E, Rosales MA, Blasco B, Rios JJ, Romero L, et al. Cytokinin-dependent improvement in transgenic PSARK::IPT tobacco under nitrogen deficiency. J Agric Food Chem 2011;59:10491–5. 120. McCabe MS, Garratt LC, Schepers F, Jordi WJ, Stoopen GM, Davelaar E, et al. Effects of PSAG12-IPT gene expression on development and senescence in transgenic lettuce. Plant Physiol 2001;127:505–16. 121. Sykorova B, Kuresova G, Daskalova S, Trckova M, Hoyerova K, Raimanova I, et al. Senescence-induced ectopic expression of the A-tumefaciens ipt gene in wheat delays leaf senescence, increases cytokinin content, nitrate influx, and nitrate reductase activity, but does not affect grain yield. J Exp Bot 2008;59: 377–87. 122. Rubio-Wilhelmi MM, Sanchez-Rodriguez E, Rosales MA, Begona B, Rios JJ, Romero L, et al. Effect of cytokinins on oxidative stress in tobacco plants under nitrogen deficiency. Environ Exp Bot 2011;72: 167–73. 123. Ashikari M, Sakakibara H, Lin SY, Yamamoto T, Takashi T, Nishimura A, et al. Cytokinin oxidase regulates rice grain production. Science 2005;309:741–5. 124. Han YY, Yang HB, Jiao YL. Regulation of inflorescence architecture by cytokinins. Front Plant Sci 2014; 5:669. 125. Zhao J, Bai W, Zeng Q, Song S, Zhang M, Li X, et al. Moderately enhancing cytokinin level by downregulation of GhCKX expression in cotton concurrently increases fiber and seed yield. Mol Breed 2015; 35:60. 126. Wang Y, Wu WH. Potassium transport and signaling in higher plants. Annu Rev Plant Biol 2013;64:451–76. 127. Ren XL, Qi GN, Feng HQ, Zhao S, Zhao SS, Wang Y, et al. Calcineurin B-like protein CBL10 directly interacts with AKT1 and modulates K+ homeostasis in Arabidopsis. Plant J 2013;74:258–66. 128. Xu J, Li HD, Chen LQ, Wang Y, Liu LL, He L, et al. A protein kinase, interacting with two calcineurin B-like proteins, regulates K+ transporter AKT1 in Arabidopsis. Cell 2006;125:1347–60. 129. Ren F, Chen QJ, Xie M, Li LJ, Wu WH, Chen J, et al. Engineering the K+ uptake regulatory pathway by MultiRound Gateway. J Plant Physiol 2010;167:1412–7. 130. Ragel P, Rodenas R, Garcia-Martin E, Andres Z, Villalta I, Nieves-Cordones M, et al. The CBL-interacting protein kinase CIPK23 regulates HAK5-mediated high-affinity K+ uptake in Arabidopsis roots. Plant Physiol 2015;169:2863–73. 131. Xue G, Lu LM, Yang TZ, Li XH, Xing XX, Xu SX. Enhanced tolerance to low-K+ stress in tobacco plants, that ectopically express the CBL-interacting protein kinase CIPK23 gene. Czech J Genet Plant 2016;52: 77–82. 132. Wang XY, Li J, Zou X, Lu LM, Li LQ, Ni S, et al. Ectopic expression of AtCIPK23 enhances tolerance against low-K+ stress in transgenic potato. Am J Potato Res 2011;88:153–9. 133. Xu J, Tian XL, Eneji AE, Li ZH. Functional characterization of GhAKT1, a novel Shaker-like K+ channel gene involved in K+ uptake from cotton (Gossypium hirsutum). Gene 2014;545:61–71. 134. Ahmad I, Devonshire J, Mohamed R, Schultze M, Maathuis FJM. Overexpression of the potassium channel TPKb in small vacuoles confers osmotic and drought tolerance to rice. New Phytol 2016;209:1040–8. 135. Nieves-Cordones M, Aleman F, Martinez V, Rubio F. The Arabidopsis thaliana HAK5 K+ transporter is required for plant growth and K+ acquisition from low K+ solutions under saline conditions. Mol Plant 2010;3:326–33. 136. Hong JP, Takeshi Y, Kondou Y, Schachtman DP, Matsui M, Shin R. Identification and Characterization of transcription factors regulating Arabidopsis HAK5. Plant Cell Physiol 2013;54:1478–90.

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137. Yang TY, Zhang S, Hu YB, Wu FC, Hu QD, Chen G, et al. The role of a potassium transporter OsHAK5 in potassium acquisition and transport from roots to shoots in rice at low potassium supply levels. Plant Physiol 2014;166 [945-U757]. 138. Song ZZ, Yang SY, Zuo J, Su YH. Over-expression of ApKUP3 enhances potassium nutrition and drought tolerance in transgenic rice. Biol Plant 2014;58:649–58. 139. Zhao J, Li PH, Motes CM, Park S, Hirschi KD. CHX14 is a plasma membrane K-efflux transporter that regulates K+ redistribution in Arabidopsis thaliana. Plant Cell Environ 2015;38:2223–38. 140. Zhao J, Cheng NH, Motes CM, Blancaflor EB, Moore M, Gonzales N, et al. AtCHX13 is a plasma membrane K+ transporter. Plant Physiol 2008;148:796–807.

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FUTURE CLIMATE CHANGE AND PLANT MACRONUTRIENT USE EFFICIENCY

Sylvie M. Brouder, Jeffrey J. Volenec Purdue University, West Lafayette, IN, United States

INTRODUCTION Nutrient use efficiency (NUE) is an integrated reflection of the soil’s ability to match nutrient supply within the root zone to plant demand and the plant’s ability to exploit soil nutrients including root zone modification. Temperature and soil moisture regimes simultaneously drive plant growth and development, and the availability and mobility of nutrients within rooting zones. As previously reviewed by Brouder and Volenec,1 primary climate change attributes relevant to NUE are increases in ambient carbon dioxide (aCO2) as a nutrient driving photosynthesis and plant biomass (shoot and root) accumulation, temperature (aerial and in soil), quantity of rainfall, and intensity and distribution of rainfall events leading to shifts in soil erosion patterns and/or changes in soil moisture status relative to plant growth and development. Both near- and long-term changes in aCO2, precipitation patterns and temperature regimes are generally considered certain to occur. Although direction of global changes in some attributes (e.g., temperature) are also considered highly certain, the Intergovernmental Panel on Climate Change 5th Assessment Report (IPCC AR5) characterizes the magnitude of any such change in the near or longterm as much less certain, and downscaled projections of any attribute for specific regions of the world are less certain still.2 Further, macronutrient representation in the crop growth models used in predicting crop yield responses to climate change remains limited, understudied and, until recently, not a priority for crop modelers seeking to improve representation of crop performance in a changed climate.3 Major models differ in the approach to and extent of their simulations of climate change factor interactions with nutrient availability4 and explicit nutrient representation is typically confined to N and P.5 Indeed a general consensus is emerging that a better understanding of plant-nutrient feedbacks is paramount in developing improved projections of climate change impacts on natural vegetation, and crop and pasture productivity.6 The central consideration for climate change impacts on NUE is whether or not a changed climate will lead to fundamental changes in our current understanding of plant–soil–nutrient interrelationships.1 Indeed, the influence of two of the three relevant climate change factors—temperature and rainfall/soil moisture—on NUE are well-documented in the scientific literature. Thus, the seminal question becomes: will further increases in aCO2 cause previously unobserved phenomena in nutrient cycling and plant-soil feedbacks? However, literature on impacts of elevated CO2 (eCO2) on NUE remain sparse reflecting the myriad of inherent challenges to establishing robust facsimiles of future Plant Macronutrient Use Efficiency. http://dx.doi.org/10.1016/B978-0-12-811308-0.00020-X Copyright © 2017 Elsevier Inc. All rights reserved.

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environments as experimental treatments. Here we review what is known about the influence of climate change variables (eCO2, soil moisture, and temperature) on: (1) soil biology, physics, and chemistry and how changes in biology, physics, and chemistry influence macronutrient supply and movement in the root zone, and (2) plant root and shoot form and function, root–shoot interrelationships, and nutrient source-sink allocation patterns within plants. Although our focus is on macronutrient efficiencies of agricultural systems, the general dearth of data on some topics necessitates reviewing literature from natural communities. We conclude with a brief discussion of emerging “climate smart” nutrient management strategies and critical knowledge gaps for plant mineral nutrition in a changing climate.

BRIEF SUMMARY OF NUE-RELEVANT IPCC CLIMATE CHANGE ASSESSMENTS TO DATE AND PROJECTIONS With respect to observed changes in the climate system, the IPCC 5AR2 characterizes warming of the planet as “unequivocal” with an increase of 0.85°C from 1880 to 2012. Concomitant increases in atmospheric CO2, to date the largest contribution to total radiative forcing, are well-documented; the 2011 levels of 391 ppm are 40% higher than preindustrial levels. In general, confidence is much lower in estimates of accompanying changes in precipitation patterns although, when compared to other latitudes, there is reasonable confidence that midlatitude areas of the Northern Hemisphere have experienced an increase especially since 1951. With respect to the occurrence of extreme weather and climate events since 1951, fewer colder and more warmer days and nights over most land areas are deemed very likely to have occurred. Increases in the frequency and duration of heavy precipitation and of drought are both considered likely in some regions of the world and an increase in intense cyclone activity is characterized as virtually certain for North Atlantic since 1970. At present, greenhouse gases causing radiative forcing are composed of 55 % CO2 and the aCO2 is expected to increase between 75% and 85% by the end of this century based on a range of future emission scenarios.7 Although the general consensus is that limiting future changes to global and regional climates will require sustained reductions in emissions of CO2 and other greenhouse gases, both the impact of various emissions reduction strategies on climate variables and the NUE-relevant biophysical, system-level responses to any future changes in those variables remain poorly understood and the subject of much debate. For example, a global surface temperature change by the end of the 21st century of 1.5°C higher relative to pre-1900 is characterized as likely based on numerous models but the IPCC AR5 report characterizes the ability of land surface and climate change models to forecast changes in soil moisture as generally fairly limited and highlights studies showing the inability of models to reproduce observed, present day correlations between precipitation and soil moisture in the tropics and other “systematic failures.”8 Indeed the predominant certainty seems to be in the anticipated lack of certainty at regional scales and below for changes in atmospheric temperature and water cycling. Thus, any projected changes in macro-NUE are currently predicated on a host of compounded uncertainties and discussion remains confined to probable scenarios based on empirical research conducted on plant genetic material adapted to the aCO2 conditions at the time of experimentation, an experimental artifact of unknown significance in climate change research.1

INFLUENCE OF CLIMATE CHANGE ON AVAILABILITY OF NUTRIENTS IN SOIL Major factors governing nutrient availability in the soil include edge-of-field losses, the quantities of soil organic matter (SOM) and microbial activity, nutrient specific chemistries controlling relationships

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between solution-phase, labile and nonlabile forms in the soil, and soil temperature and moisture. The soil fertility/plant nutrition scientific literature extends back more than a century with many older studies having strong topical relevance to understanding potential influences of climate change. For example, the main body of literature contributing to our understanding of the role of soil moisture in controlling root-zone nutrient availability dates from seminal work conducted by Nye, Tinker and Barber (e.g., Barber9,10 and references cited therein). However, given the disparate purposes of the original studies, care must be taken in their interpretation for the context of global climate change. Not only are many of the studies focused on a single factor but they tend to use only a few, discrete and markedly different conditions for key independent variables, such as temperature (e.g., 15 vs. 29°C11).

Potential for reduced NUE from increased edge-of-field losses Edge-of-field loss mechanisms for nutrients include leaching, runoff and erosion, and volatilization from the soil surface (N only in arable soils); losses by any of these mechanisms attributable to climate change will impact uptake efficiency by direct reduction of soil nutrient availability and thus may reduce all agronomic characterizations of NUE (Fig. 20.1). Field-scale P loss has been strongly linked to run-off and erosion of surface soils although leaching losses of P to below the root zone can be an

FIGURE 20.1  Measures of Nutrient Use Efficiency (NUE) and Potential Impacts of Climate Change Variables on Soil and Plant Attributes Relevant to NUE Symbols ↑, ↓, ↔, →, θ, and T indicate “increase,” “decrease,” “alteration but direction uncertain,” “effect causes,” “soil moisture content,” and “temperature,” respectively. Adapted from Brouder SM, Volenec JJ. Impact of climate change on crop nutrient and water use efficiencies. Physiol Plant 2008;133:705–241; Dobermann A. Nutrient use efficiency-measurement and management. In: Fertilizer Best Management Practices. 1st ed. IFA:Paris, France; 2007. pp. 1–28.21

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important loss mechanism in soils, with low P sorption capacity and/or with high sorption soils but artificial drainage,12 especially during winter in colder climates.13 In both cultivated and uncultivated soils, nutrients tend to be highly stratified with markedly higher macronutrient contents in most surface soils reflecting residue return patterns and fertilization strategies.14 The expected climate changerelated increases in extreme events including high intensity precipitation are forecast to increase runoff and erosion15,16 and can be expected to affect the mobilization and transport of P and other nutrients by carrying solubilized forms, surface-applied fertilizers and soil particles with nutrients complexed to organic matter, chemically adsorbed or held electrostatically on exchange surfaces.17,18 Likewise, any increase in precipitation with a concomitant increase in infiltration that leads to soil moisture contents in excess of field capacity can result in leaching losses of solubilized nutrients from moderately to well-drained soils. In soils with net negative charge (common), cationic nutrients (NH +4 , K+, Mg2+, and Ca2+) will be retained on soil surfaces, while major nutrient anions (NO −3 , and SO 2− 4 ) will be easily flushed.16,19,20 In soils with low net negative charge (e.g., sand) or a net positive charge (e.g., soils containing weathered kaolin minerals, iron and aluminum oxides, and amorphous materials), nutrient cations may be subject to leaching; regardless of charge, frequent flushing of soil profiles will significantly deplete all macronutrients. For poorly drained soils, increases in frequency and duration of periods of soil saturation can increase biological denitrification and emission of nitrous oxide (N2O), a potent greenhouse gas, and N222 but more research is needed to understand how climate change-related temperature and moisture shifts will impact emissions.16 Further, nitrogen oxides (NOx) are formed in the same processes as for N2O (nitrification and denitrification) and are therefore influenced by the same climate change factors (temperature and moisture16). Finally, volatilization of N as NH3 is most typically associated with broadcast applications of fertilizer but can be produced by mineralization of plant residues and organic matter. In the absence of a fertilizer application, it is unclear whether the changes in the soil and atmospheric temperatures, and soil moisture levels associated with climate change will shift volatilization patterns significantly.

Climate change impacts on soil organic matter and biogeochemistry

SOM is commonly used as an indicators of soil quality (e.g., Sequeira and Alley23; and Congreves et al.24) and, because of the quantitative importance of N both in plant growth and crop yields, and in environmental degradation including N2O emission as direct driver of climate change, much of the climate change/plant nutrition literature to date has focused on climate change impacts on C:N biogeochemical cycling.2,15,16,25,26 However, SOM also stabilizes soil aggregates, improves infiltration and retention of water in the soil, supports microbial activity, provides an adsorbing surface for nutrient retention and pH buffering, and serves as a primary source of plant available P and S, as well as N; thus any changes in soil C stocks will have broad implications for general soil fertility and NUE in addition to direct influences on trajectories for radiative forcing. The general trend for geography of SOM reserves in uncultivated soils is for higher contents with cooler temperatures, and, given similar temperatures, more precipitation is conducive to more plant biomass and therefore more SOM.27 To date, the literature on climate change impacts on soil C stocks is contradictory28 although experts agree that climate change will alter the flux of C belowground.29 Some have postulated that warmer soils and/or enhanced rhizosphere priming with eCO2 will accelerate SOM mineralization thereby reducing soil C stocks and the associated soil health and quality benefits.15,30 Others have argued for enhanced soil C sequestration primarily from the eCO2 fertilization

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effect leading to bigger plants with more and/or finer roots (discussed in greater detail below) provided net primary productivity of plants is, itself, not nutrient limited31 and from alterations in microbial turnover that promote versus decrease soil C with warming.32 When considering the potential for soil to sequester C and mitigate atmospheric increases of CO2, Powlson et al.33 identify as reasons for uncertainty in the effect of climate change on soil C stocks: (1) a general misunderstanding of C flows and (2) uncertainty regarding the extent to which decomposition of organic C in soils will be altered by climate change factors/parameters. Certainly, these uncertainties extend to discussions regarding SOM contributions to meeting plant nutrient requirements and the agronomic efficiencies of fertilizers applied to augment SOM-sourced nutrients. At global scales, the IPCC AR5 reports a majority of models project a net increase in C uptake by land ecosystems but cite a low confidence in the magnitude of future land C changes. In natural systems, soil C storage is anticipated to be limited by nutrients, primarily N and P.26,31 Wieder et al.31 remarked that explicit consideration of P limitations in global models remains uncommon and failure to represent N and P constraints in terrestrial C cycling represents a major knowledge gap. In studying the plant-available N and P in grasslands, Dijkstra et al.34 found eCO2 increased P availability to plants and microbes relative to N whereas warming had the reverse effect. A recent synthesis by Terrer et al.35 highlights the need to incorporate mycorrhizae into global climate models in order to understand feedbacks between nutrient cycling, atmospheric CO2 and soil C stocks. In a related review, Dijkstra et al.36 examined rhizosphere priming by the enhanced root activity with eCO2; results suggested that increased priming associated with higher aCO2 may enhance SOM decomposition and plant-available N in N-limited systems but in P-limited systems may promote dissolution/mobilization of mineral P versus mineralization of organic P. Kirkby et al. have repeatedly demonstrated the predictable stoichiometry of C:N, C:P, and C:S ratios required for stable storage of C in arable soils37,38 suggesting that, like N and P, S availability also controls C cycling and soil C stocks. Collectively, such studies support the need for climate change research to consider other nutrient cycles in addition to the terrestrial N cycle for feedbacks to plant productivity and C sequestration. Similarly, for temperature impacts on soil C sequestration, Conant et al.39 concluded that in order to predict the fate of soil C stocks in a warmer world advances in understanding are needed for a host of temperature-sensitive soil processes and reactions from microbial efficiency to enzyme production.

Climate change impacts on availability of macronutrients of mineral origin Quantitatively, the vast majority of the nutrients acquired by roots are taken up from the soil solution, the water occupying soil pore space, typically from 15% to 35% (volume water/volume soil10). Whereas soil solution concentrations of nutrients of organic origin are controlled by mineralization and immobilization of SOM, adsorption-precipitation and desorption-dissolution reactions govern solution-phase concentrations of macronutrients primarily sourced from minerals.1 Well-established theory and concepts governing physiochemical behavior of nutrients in the soil have long-identified 2+ soil temperature and moisture as key drivers of soil solution concentrations of K+, H 2 PO −4/HPO 2− 4 , Mg , 2+ and Ca . Buffer power is the functional characterization for the relationship between solution–phase and labile or plant-available solid–phase of nutrients in the soil.10 Although the term has been used to capture mineralization of SOM, it is most typically applied to solid–solution phase interactions that do not involve SOM mineralization. Any functional importance of the direct effects of climate change-related temperature increases on buffering of soil solution phase ions by labile solid-phase ions in arable soils is, at best, highly

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speculative. Reaction rates of nutrient solutes in soil systems are temperature dependent and, in some cases, large temperature differences have been documented to significantly alter the quantities of solidphase ions necessary to maintain a given concentration of solution-phase ion. For example, in a low fertility soil, Ching and Barber11 found that raising soil temperature from 15 to 29°C reduced by almost 50% the buffer value of solid phase for solution phase K+ (i.e., lower quantities of solid-phase nutrients were necessary to replenish solution-phase concentrations of nutrients extracted by roots). However, such temperature effects were greatly reduced with even small additions of K fertilizer and could be negligible provided fertility is sufficient and maintained suggesting that in a warmer climate comparatively lower fertility levels might be sufficient. In contrast, Mackay and Barber40 found only a minimal but opposite effect of temperature (18 vs. 25°C) on P buffering in a low-P soil. Barrow41 reviewed literature on the effect of temperature on sorption of ions by soil and other constituents and concluded that the literature to-date was generally consistent in showing higher temperatures decreased adsorption of anions and increased adsorption of cations. Pilbeam42 used this analysis and references cited therein to argue that increases in soil temperature associated with climate change will reduce plant availability of applied P via reduced P adsorption in labile versus immobilized forms perhaps creating an opportunity for breeding strategies that select for proliferation of fine roots in upper profiles of soils. While Pilbeam42 does note that the impact of this effect on plant nutrition must also consider mycorrhizal associations, it is arguably more important to note that the experimental conditions, and thus the inference space of the buffering and sorption experiments, may not be particularly relevant to projecting important changes in NUE linked to climate change. As for the Ching and Barber study,11 the literature summarized by Barrow41 involved comparisons of large temperature differences (5, 25, and 40°C43; 20 and 40°C44); further, some of the cited studies were of reactions with relatively pure clays and hydrous oxides (e.g., Haseman et al.45) versus whole soils. It remains unclear that such effects should be major considerations as compared to climate change impacts on soil moisture status and the consequent, combined effects of soil moisture and temperature on nutrient movement from bulk soils to root surfaces and the function of the root system. Equilibria between solid- and solution-phase can also be impacted by the magnitude of wet-dry soil cycling and freeze-thaw regimes. During periods of low soil moisture content, ions are concentrated in the reduced volume of the soil solution and this can strongly impact equilibrium reactions. As reviewed by Brouder,46 many have theorized that drying can warp clay plates and/or peel back edges of the particle layers permitting greater access to inner layer exchange sites with subsequent rewetting. In low-K soils, this has generally been observed to result in the release of fixed or nonexchangeable K reserves to more labile forms. However, when fertilizer K is added, extreme drying of soils may result in net K fixation and a consequent loss from the exchangeable or labile pool46 thereby reducing plant K uptake and fertilizer efficiency (agronomic NUE; Fig. 20.1).47,48 With larger ionic radii, Mg2+ and Ca2+ are not prone to this effect but NH +4 has a similar ionic radius to K+ and is therefore subject to fixation at positions within clay layers during drying if not rapidly transformed to nitrate. Finally, to the extent that climate change enhances freeze-thaw regimes, physical weathering of clay minerals will increase and this may increase plant availability of cationic nutrients (e.g., Walworth49). Nutrient ion diffusivity in soil solution is strongly dependent on both ambient temperature and soil moisture content, and it is reasonable to expect that climate change may alter this critical aspect of rootzone nutrient availability. Even for densely-rooted plants in the soil layer of greatest root exploration, the proportion of the soil volume physically occupied by roots is low (approximately 1% for topsoil14). Thus, most nutrients taken up by plants must first move at least a few millimeters from the bulk soil to

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the root surface; the mechanisms for this movement are mass flow—convective movement of solutes with the soil water driven by plant transpiration—and diffusion down a concentration gradient created by depletion of solution nutrients with root uptake.9 Although some nutrients (e.g., Mg2+ and Ca2+) have been consistently documented to arrive at root surfaces predominantly by mass flow, this is not an attribute of the nutrient itself but of a common condition of fertile soils whereby soil supplies of these nutrients are generous relative to plant demand. Mass flow and diffusion operate in tandem; should a plant experiencing reduced transpiration due to eCO2 (discussed below) also express a nutrient deficiency, diffusivity of that nutrient in the soil solution can be assumed to be inadequate to meet plant demand.1 Effective nutrient diffusivity in the soil is usually characterized as movement of the ion within a planar or 2-dimension surface (cm2 s−1) and is a direct function of the nutrient’s diffusivity in water as characterized by Fick’s law (temperature dependent) but modified for soil medium. Modifications reflect the proportion of the soil volume occupied by water, the length of the diffusion path as obstructed by soil particles and air pockets, and the physiochemical effects of the solid phase on nutrient movement.10 Ching and Barber11 demonstrated that effective diffusivity of K+ was increased 1.5-fold at 30°C as compared to 15°C [corresponds to a 60% increase in linear (cm d−1) diffusivity; Fig. 20.2] and this temperature-related difference could be overcome by adding 50 mg K fertilizer per kg of soil (approximately 130 kg K per ha incorporated to a depth of 0.2 m). With respect to soil moisture, Mackay and Barber50 found increasing soil volumetric content from 0.22 to 0.32 (v/v) increased effective diffusivity of P by 1.2- to 1.3-fold across three different soil types; similarly, Mackay and Barber51 observed a difference of 1.1-fold in K diffusivity for the same soils and moisture treatments. As for temperature, increasing fertility can overcome some of the negative impact of low soil moisture content on diffusivity but cannot be expected to overcome concomitant alterations to root development associated with drought stress.1 In sum, climate change-driven changes in soil moisture including increases in prolonged drought may ultimately be relatively more influential than associated changes of a few degrees in soil temperature but such a conclusion is speculative given the uncertainty of projections for future soil conditions at any scale. Regardless, simulation modeling suggests that for relatively immobile nutrients uptake may be more strongly influenced by associated changes in root geometry and soil exploration (discussed below) than by soil factors per se.

IMPACT OF CLIMATE CHANGE ON SHOOT AND ROOT GROWTH, NUTRIENT UPTAKE, AND PHYSIOLOGICAL NUE Plant-related drivers of nutrient uptake and NUE include the rate of plant growth that represents the “sink strength” for a particular nutrient,1 and root development that enhances the surface area through which nutrients are transported.10 As water and nutrients required by shoots are initially acquired by roots, development of the above- and below-ground organs of most plants is synchronized and is characterized by root:shoot ratio. Difficulty in measuring root traits of soil-grown plants limits the accuracy of root:shoot estimates. As a result, the generally positive impact of eCO2 on root growth of most species in free air CO2 experiments (FACE) and open-top chamber studies remains poorly quantified.52–54 In addition, estimates of root biomass and root:shoot ratios have limitations because they generally do not capture the role of root hairs and other unique root structures, such as proteoid roots, in nutrient uptake and crop yield.55 Finally, it is known that there are differences in nutrient uptake per se along the root itself. These complications aside, root mass and root:shoot ratio remain the standard of belowground biomass measures made in climate change studies.

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FIGURE 20.2  Estimated Linear Diffusion Rates of K in Soil at 15 and 29oC as Influenced by K Addition Rates Linear diffusion for a given temperature and K rate calculated from effective diffusion coefficients (De, cm2 s−1) presented in Ching and Barber.11 The reference line shows a typical De value for K in soils10 Figure reproduced from Brouder S. Potassium Cycling. In: Hatfield JL, Sauer TJ, editors. Soil Management: Building a Stable Base for Agriculture. American Society of Agronomy:Madison WI; 2011. pp. 79–10246, with permission from the American Society of Agronomy and the Soil Science Society of America.

Physiological aspects of NUE can be expressed in several ways (Fig. 20.1) with internal use efficiency (kg biomass/kg nutrient in biomass) being the most common concept reported in climate change research (discussed later). Estimates of NUE vary with nutrient, prevailing environment and management, and organs selected for nutrient analysis with large differences often observed between vegetative versus reproductive tissues. For example, vegetative tissues of many crop plants will exhibit luxury consumption of N and K when these nutrients are present in soils at high concentrations56 resulting in low NUE. Nevertheless, when nutrients are supplied in adequate, but not excessive levels, NUE remains a useful index for understanding the interacting effects of environment, plant species/genotype, and agronomic management on productivity and nutrient use.

Shoot form and function Understanding macro-NUE relies, in part, on characterizing the impact of eCO2 on shoot growth responses of plants, and placing these responses in the context of plant nutrition. Earlier reviews57,58

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predicted that doubling CO2 levels would result in an average yield increase of 33% over plants grown in aCO2. Interactions of soil fertility with atmospheric CO2 level suggested that plant growth benefits associated with eCO2 would be limited in low fertility soils and that developing nations would be less likely to capture these CO2-driven yield increases.1 Most data on yield-eCO2 responses in these earlier studies focused on plants with C3 photosynthesis. Marks and Clay59 and Conroy60 conducted side-by-side comparisons of C3 and C4 plants and showed that eCO2 did not enhance shoot dry matter production of C4 plants. As a result, little change in mineral nutrition of C4 plants is anticipated. This speculation was confirmed in a recent metaanalysis that reported no change in tissue mineral concentrations of C4 plants in response to eCO2.61 Analysis of a comprehensive data set published by Dietterich et al.62 illustrates species-specific variation in mineral concentrations of cereals (both C3 and C4) and C3 legumes when grown in FACE (Table 20.1). eCO2 consistently reduced grain N concentrations of both wheat and rice (C3 cereals), while tissue N concentrations of C4 cereals and C3 legumes were not altered by eCO2. Unlike N, concentrations of P and K (and most other nutrients) were largely unaffected by eCO2 in any group including the C3 cereals. This suggests that as a group nonleguminous C3 plants are more likely to exhibit changes in macro-NUEs than C4 plants and C3 legumes. The physiological basis for these group-related differences are discussed later in more detail, but appear to be associated, in part, with the interaction between photorespiration and nitrate reduction.63 Because of this differential response to eCO2, most of the remaining discussion will focus on studies using nonleguminous C3 plants. We previously summarized the impact of climate change on crop NUEs concluding that, in general, nutrient removal will scale with yield increases associated with eCO2.1 However, subtle, but potentially important variation in tissue nutrient concentrations and total nutrient uptake are likely to occur, and awareness of these changes will be important for devising appropriate nutrient management strategies in a changing climate. For example, Conroy60 reported that the critical concentration (the tissue nutrient concentration required for maximum yield) of P was higher for wheat, eucalyptus, and cotton in eCO2. The eCO2-enhanced yields of cotton and eucalyptus were optimized at higher fertilizer N applications and these high yields were obtained at lower critical N concentrations than plants grown in aCO2. By comparison, maximum wheat yields were obtained at the same N fertilizer rate irrespective of CO2 level, but like cotton and eucalyptus, the high yields of wheat in eCO2 were obtained at lower tissue critical N concentrations. Similarly, while fertilization enhanced most eCO2 responses of four perennial grasses, Goverde et al.64 also observed several species x CO2 interactions for both growth and compositional traits, but not tissue N concentration. The underlying reasons for plant-specific response of perennial C3 grasses to eCO2 are not clear, but may include species- and even cultivar-specific symbiotic/mutualistic associations with endophytic fungi that have been shown to enhance growth and general stress tolerance of their host plant; a benefit that also appears to extend to NUE in eCO2. Newman et al.65 observed complex interactions of N level and endophyte infection for growth responses and N-use efficiency of tall fescue at eCO2. For example, while leaf N of tall fescue declines under eCO2, endophyte-infected tall fescue exhibits less of a decline in leaf N and maintains higher leaf photosynthetic rates and greater growth when compared to endophyte-free plants. Similar interactions were reported for perennial ryegrass where endophyte-infected plants yielded more (P = 0.07) than endophyte-free plants but only at eCO2 and high N.66 While the underlying mechanism(s) of these responses is unclear, this illustrates how unforeseen factors (e.g., fungal mutualism) can impact growth and NUEs of plants in a changing climate, and provides a largely unexplored alternative approach for plant adaptation to climate change.

Table 20.1  Impact of Ambient (aCO2) and Elevated (eCO2) Atmospheric Carbon Dioxide Concentrations on Nutrient Concentrations (Mean and Standard Error) of Edible Tissues of C4 Cereals, and C3 Cereals and Legumes C4 Cereal Maize (n = 18)

Sorghum (n = 15–20) eCO2

aCO2

eCO2

Rice (n = 111–132) aCO2

eCO2

Soybean (n = 104–113) aCO2

eCO2

Field Pea (n = 43–45)

aCO2

N (g kg )

10.44 ± 0.36 10.00 ± 0.29 19.03 ± 0.91 19.65 ± 1.05 29.95 ± 0.21 28.04 ± 0.21 13.05 ± 0.11 12.00 ± 0.09 63.3 ± 0.32 63.3 ± 0.23 33.92 ± 0.35 33.20 ± 0.25

P (g kg )

2.52 ± 0.05

2.35 ± 0.04

3.61 ± 0.17

3.75 ± 0.20

3.90 ± 0.04

3.76 ± 0.04

3.44 ± 0.03

3.40 ± 0.02

5.81 ± 0.05 5.74 ± 0.05 3.36 ± 0.07

K (g kg−1)

3.30 ± 0.05

3.21 ± 0.05

4.62 ± 0.14

4.75 ± 0.14

4.96 ± 0.04

5.01 ± 0.03

3.02 ± 0.03

3.05 ± 0.03

19.2 ± 0.09 19.2 ± 0.09 10.20 ± 0.18 10.37 ± 0.15

Ca (g kg−1)

0.05 ± 0.005 0.05 ± 0.002 0.20 ± 0.016 0.23 ± 0.017 0.51 ± 0.007 0.48 ± 0.007 0.12 ± 0.002 0.12 ± 0.002 2.93 ± 0.05 2.78 ± 0.05 0.68 ± 0.024 0.68 ± 0.022

−1

aCO2

Wheat (n = 226)

C3 Legume

Nutrient −1

eCO2

C3 Cereal

aCO2

eCO2

3.21 ± 0.05

Mg (g kg−1) 0.94 ± 0.017 0.89 ± 0.018 1.56 ± 0.06

1.61 ± 0.07

1.43 ± 0.01

1.34 ± 0.01

1.34 ± 0.010 1.32 ± 0.008 2.33 ± 0.02 2.24 ± 0.02 1.11 ± 0.008 1.10 ± 0.007

S (ppm)

919 ± 16

947 ± 15

1168 ± 67

1207 ± 70

1903 ± 10

1809 ± 10

993 ± 9

917 ± 8

3123 ± 13

3024 ± 12

1826 ± 22

1772 ± 17

Zn (ppm)

19 ± 0.5

18 ± 0.4

27 ± 1.9

28 ± 2.0

31 ± 0.4

28 ± 0.4

28 ± 0.4

27 ± 0.3

40 ± 0.4

38 ± 0.3

26 ± 0.5

24 ± 0.5

Fe (ppm)

15 ± 0.5

14 ± 0.6

32 ± 2.1

34 ± 2.3

41 ± 0.4

39 ± 0.4

11 ± 0.2

11 ± 0.1

76 ± 0.5

73 ± 0.5

42 ± 0.5

40 ± 0.5

B (ppm)

4.30 ± 0.05

4.53 ± 0.12

5.94 ± 0.35

6.19 ± 0.36

1.80 ± 0.05

1.77 ± 0.05

2.13 ± 0.04

2.24 ± 0.04

46.0 ± 1.0

42.6 ± 0.4

7.06 ± 0.06

6.98 ± 0.06

Mn (ppm)

3.83 ± 0.09

3.66 ± 0.10

25.0 ± 1.0

26.3 ± 1.2

49.4 ± 0.50

47.9 ± 0.49

35.5 ± 1.1

32.5 ± 0.9

27.9 ± 0.4

27.6 ± 0.4

9.36 ± 0.16

9.03 ± 0.12

Cu (ppm)

2.09 ± 0.10

1.94 ± 0.06

3.90 ± 0.28

3.97 ± 0.29

5.62 ± 0.04

5.42 ± 0.05

3.06 ± 0.07

2.68 ± 0.06

13.8 ± 0.24 13.1 ± 0.14 5.90 ± 0.11

5.72 ± 0.09

The number of observations (n) for each species is provided. Data for this analysis were previously published by Dietterich et al.

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FIGURE 20.3  Nitrogen (N) Use Efficiency (Filled Bars, g Dry Matter/g N in Dry Matter) and N Yield (Open Bars, g N m−2) as Influenced by Carbon Dioxide Level (aCO2 = Ambient, avg. 376 ppm; eCO2 = Elevated, Avg. 549 ppm) and N Application (100 = Adequate, 50 = Reduced N) Numerals above bars are tissue N concentrations in percent of dry matter. Data were averaged over species (barley, ryegrass, sugar beet, and wheat) grown in a free-air, CO2 experiment in Braunschweig, Germany. Adapted from Weigel HJ, Manderscheid R. Crop growth responses to free air CO2 enrichment and nitrogen fertilization: rotating barley, ryegrass, sugar beet and wheat. Eur J Agron 2012;43:97–107.67

Due to its importance in agriculture and the negative effect of eCO2 on tissue N concentrations (Table 20.1), interactions of atmospheric CO2, with N-use efficiency and the underlying mechanisms associated with these changes have received more research attention than eCO2 impacts on P and K nutrition. Internal NUE (kg dry mass produced/kg nutrient acquired; Fig. 20.1) is often higher under eCO2 (Fig. 20.367). This occurs, in part, because of eCO2-enhanced dry matter production may occur, with little or no change in total N acquisition (N yield) by the plant. This results in lower tissue N concentrations and higher internal NUE under eCO2. Reductions in tissue N concentrations at eCO2 were originally thought to be associated with simple dilution of plant N by eCO2-stimulated growth.68,69 However, a recent metaanalysis using data from FACE facilities70 revealed that N mass per plant decreased as much as 10% at eCO2 even when plant growth was essentially unchanged. In addition, this analysis showed that lower tissue N concentrations in eCO2 could not be increased to levels observed in aCO2 by simply adding more N fertilizer (Fig. 20.3). This also agrees with Conroy60 who reported a reduction in shoot N concentrations of C3 plants at eCO2 in spite of very high N fertilizer application rates. Analysis of FACE data published by Dietterich et al.62 agrees with these conclusions (Table 20.2). While the positive effect of increasing N fertilizer on grain N concentrations is apparent, the reduced grain N concentrations of rice and wheat grown at eCO2 were not alleviated by simply adding more N fertilizer. Thus, the low tissue N concentrations, reduced total N uptake, and greater internal NUE of C3 plants grown in eCO2 appears to be due to factors other than growth dilution. The physiological basis for lower shoot N concentrations under eCO2 even when ample fertilizer N is present could be due to a combination of factors including reduced N uptake, slow N translocation from roots to shoots, and/or reduced N assimilation in shoots. Unfortunately there is a dearth of information available to inform these questions, but what is published may guide future research directions. eCO2 did not influence NH +4 -N uptake (as 15N) by excised roots of N-deficient Agrostis capillaris

Table 20.2  Impact of Nitrogen Fertilizer Application and Atmospheric CO2 (aCO2 = Ambient; eCO2 = Elevated) on Concentrations (Mean and Standard Error) of Nitrogen (N), Phosphorus (P), and Potassium (K) in Edible Tissues of Rice and Wheat Rice 0 kg N ha−1 (n = 4) Nutrient (g kg−1) aCO2 eCO2

80 kg N ha−1 (n = 77–78) aCO2

eCO2

Wheat 120 kg N ha−1 (n = 50) aCO2

eCO2

0 kg N ha−1 (n = 208) aCO2

eCO2

50 kg N ha−1 (n = 48) aCO2

eCO2

N

10.60 ± 0.17 10.08 ± 0.11 12.85 ± 0.13 11.88 ± 0.10 13.55 ± 0.15 12.34 ± 0.15 29.85 ± 0.24 27.85 ± 0.24 30.39 ± 0.46 28.89 ± 0.45

P

3.24 ± 0.02

3.25 ± 0.03

3.48 ± 0.04

3.37 ± 0.03

3.40 ± 0.05

3.46 ± 0.05

3.95 ± 0.04

3.80 ± 0.04

3.68 ± 0.09

3.58 ± 0.08

K

2.98 ± 0.05

3.00 ± 0.00

3.05 ± 0.05

3.00 ± 0.03

2.99 ± 0.04

3.12 ± 0.04

4.95 ± 0.04

5.01 ± 0.04

4.96 ± 0.06

5.02 ± 0.08

The number of observations (n) for each species-environment combination is provided. nitrogen fertilizer application does not overcome the reduction in tissue N concentrations that result from eCO2. Data for this analysis was previously published by Dietterich et al.62

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plants when compared to that of plants grown in aCO2.71 Arndal et al.72 also using 15N-labeling likewise reported no consistent impact of eCO2 on NH +4 or NO −3 uptake per unit of root length of native grassland and heathland species grown at a low fertility site and exposed to eCO2 for 3 years. However, recent reports indicate that N uptake might be altered when both temperature and CO2 are elevated. Using an N mass balance approach in hydroponics Jayawardena et al.73 reported reductions in both NO −3 and NH +4 uptake of tomato grown at 37°C and 700 ppm CO2 when compared to plants grown at 30°C and 700 ppm CO2. Based on N concentration patterns in roots and shoots these authors also suggested that translocation of N from roots to shoots of tomato was reduced by eCO2. Even if N uptake and translocation to shoots is not disrupted, there is evidence that assimilation of N, and in particular NO −3 , may be disrupted under eCO2 conditions. Bloom et al.63 showed marked reductions in assimilation of absorbed NO −3 by wheat grown under eCO2 conditions. Later work suggested a biochemical linkage between low photorespiration of C3 plants growing in eCO2 and substrates + 74 and cofactors required to reduce SO 2− Subsequent studies with wheat grown with 4 to NH 4 in leaves. + − NH 4 instead of NO 3 as the primary N source resulted in greater growth and higher shoot N concentrations at eCO2; an advantage not observed at aCO2.75 These authors suggested that use of NH +4 -based N fertilizers, nitrification inhibitors, and similar technologies may improve N assimilation, growth, and N composition of C3 cereals under eCO2; concepts that await validation at-scale in the field. Few studies have examined in detail the impact of eCO2 on P and K concentrations and NUE of these nutrients. Concentration of P and K in edible tissue of several agronomic species appears to be largely insensitive to eCO2 (Table 20.1). Conroy62 reported that tissue P accumulation in Pinus radiata and soybean scaled with eCO2–driven increases in plant dry weight. Neither species responded to eCO2 at low soil P levels, but yield nearly doubled when adequate P was present under eCO2 conditions. Unlike tissue N concentrations, foliar P concentrations of these species were not reduced under eCO2. In contrast, growth of Eucalyptus grandis increased with eCO2 irrespective of soil P level and foliar P concentrations were reduced by one-half under eCO2 conditions. Although eCO2 enhanced average plant dry weight 11% over growth in aCO2, Newbery et al.71 reported no impact of eCO2 on tissue P concentrations in Agrostis capillaris. Linear regression of tissue P concentrations in aCO2 versus eCO2 had a slope of 0.85 and an intercept essentially equal to zero (Fig. 20.4A). This indicates that the sixfold difference in tissue P concentrations was influenced largely by fertilizer application, and this relationship was not altered by eCO2. Likewise, regression of tissue K concentrations in aCO2 versus eCO2 also revealed a slope of approximately 1 indicating a similar response to K fertilization (Fig. 20.4B). However, tissue K concentrations in eCO2 were consistently lower than those in aCO2. NUEs for K in eCO2 would be higher than aCO2 because of the greater yield that occurred with lower tissue K concentrations. Additional work on P and K accumulation in both vegetative and reproductive tissues of major agronomic crops is needed in order to draw definitive conclusions regarding the impact of eCO2 on uptake and NUE. Tissue nutrient concentrations can be used with yield response data to determine critical nutrient concentrations (the nutrient concentration where 95% of maximum yield is achieved). This value is a crop- and tissue-specific diagnostic tool used to inform fertilizer management strategies. For example, the critical K concentration in the uppermost 15 cm of flowering alfalfa shoots is 2.25% of dry matter76; K applications are generally recommended for alfalfa stands where tissue K levels fall below this concentration. It is not known if climate change, including eCO2 will alter critical concentrations for macronutrients of major crop species, but changes in yield-nutrient concentration relationships mentioned above make this a possibility. For example, lower critical concentrations for N would be anticipated

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FIGURE 20.4  Relationship Between Concentrations of Phosphorus (P) and Potassium (K) in Aboveground Tissues of Agrostis capillaris Grown in Ambient and Elevated (Ambient plus 250 ppm) Carbon Dioxide (CO2) Nutrient solutions provided plants differed in P and K, and resulted in large differences in tissue nutrient concentrations in both CO2 environments. Adapted from Newbery RM, Wolfenden J, Mansfield TA, Harrison AF. Nitrogen, phosphorus and potassium uptake and demand in Agrostis capillaris: the influence of elevated CO2 and nutrient supply. New Phytol 1995;130:565–74.71

where higher yields but lower tissue N concentrations occur under eCO2 (Fig. 20.5). Such shifts in yield-N concentration relationships have been reported. Conroy60 reported a reduction from 4.3% to 3.3% in the critical N concentration of wheat when grown at eCO2. Alternatively, nutrient accumulation rates that exceed growth rates in eCO2 result in higher tissue nutrient concentrations and can result in higher critical concentrations (Fig. 20.5). Conroy60 reported that the critical P concentration of Pinus radiata increased from 0.62% to 1.00% when plants were grown in eCO2. While firm conclusions regarding climate change effects on critical nutrient concentrations requires more experimentation and data, including broader representation from other crops, soils and nutrients, understanding

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FIGURE 20.5  Hypothetical Relationship Between Nutrient Concentration and Yield in a Changing Climate Yields are plotted as a percent of maximum yield. The critical nutrient concentration is defined as the nutrient level which achieves 95% of maximum yield (horizontal line). The solid curve represents the current yield-nutrient concentration relationship at aCO2. The other curves represent scenarios where the critical nutrient concentration is lower (small dashes) or higher (large dashes). Adapted from Conroy JP. Influence of elevated atmospheric CO2 concentrations on plant nutrition. Austr J Bot 1992;40:445–5660 and references cited therein.

these relationships is fundamental to global food security and nutrient stewardship and as such should be a high priority.

Root form and function While there is a general agreement that eCO2 increases aboveground biomass, especially in C3 cereals, our understanding of how eCO2 alters root form and function is far less complete. Although data are scant, root growth responses of C4 plants to eCO2 (+15%) is generally less than that observed for C3 plants (+44%).77 A previous literature review78 suggested that, while root growth of most species studied increased with eCO2, there was no consistent change in direction or magnitude of response for specific root traits relevant to nutrient uptake including mycorrhizal infection, root-shoot ratio, nutrient absorption capacity, NUE, or alterations in root morphology. Inconsistencies regarding root growth responses to eCO2 may reflect the difficulty of measuring root traits and the indirect manner in which these data are often acquired, that is, as standing root biomass at a moment in time from a relatively small, spatially discrete soil column, and not root growth rates per se. Root biomass is the net difference between root growth rates and root mortality, and this latter characteristic also has been found to be influenced by eCO2. For example, in a perennial grass ecosystem

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Carrillo et al.79 observed that greater net root mass under eCO2 resulted from both increased root production and slower rates of root death. Such root growth responses to eCO2 may be species-specific, and more prevalent in perennial plants (forests, and grasslands) than the annual plant species that dominate many agroecosystems55; however, even responses within perennial species can vary80 suggesting that factors, in addition to eCO2-stimulated shoot growth and root:shoot ratios impact root development. For example, root model simulations suggest that the reduced root N concentration with eCO2 minimizes root protein turnover resulting in low maintenance dark respiration rates that, in turn, increase root longevity and ultimately lead to greater root biomass.81 Other climate change factors like elevated soil temperature and reduced soil water, in addition to eCO2, impact both growth and death rates of roots and final root biomass.82,83 While drought and elevated temperatures generally reduced root biomass and growth in these studies, these reductions were more than offset by the growth increases associated with eCO2.72,84 Enhanced root growth in response to eCO2 is associated with changes in root morphology. In a recent review, Madhu and Hatfield53 indicated that most root morphological traits including number, diameter, length, and growth rate increased in many species in response to eCO2. For example, eCO2 plus artificial warming of native grass ecosystems to mimic climate change produced roots that were longer, thinner and had greater surface area, and resulted in greater biomass which could potentially enhance nutrient uptake.79 This result agrees with findings with other perennial grasses,80,82 several C3 crops species,84 and C3 plants in general.85 Based on review of the literature the latter authors concluded that changes in root length and the accompanying deeper rooting generally resulted in enhanced P uptake of several crop species. Mechanistic models use root morphology to understand the relative importance of soil and plant factors governing nutrient uptake. From these simulations traits like root length density have emerged as having particular importance in the nutrient uptake process.1 Because root mass and morphology are altered by eCO2 it follows that nutrient uptake at the root-level also could be altered by climate change. For example, NO −3 and especially NH +4 uptake per unit root length of grassland species were generally greater with eCO2.72 Labeling studies with Agrostis spp. grown in eCO2 indicate that N (as 15N) and P (as 32P) uptake rates by detached roots generally scale with eCO2-stimulated plant growth rates.71 Altering nutrient solution P concentrations changed tissue P concentrations in the expected manner whether plants were grown in eCO2 or aCO2 (Fig. 20.4). However, uptake of K predicted using 86Rb (as a K analog) at the root level gave inconsistent results. While uptake of 86Rb by detached roots was greater under eCO2 when compared to aCO2, tissue K concentrations were consistently lower in aboveground tissues of plants grown in eCO2 (Fig. 20.4). It is not clear if this is a K-specific response to eCO2, such as a decrease in transpiration and K transport to shoots, or is an artifact of the methods used to assess K uptake (86Rb, detached roots). However, impaired translocation of N from roots to shoots in eCO2 environments has recently been reported by others.73 Thus, both nutrient uptake by roots and partitioning to shoots can be altered by eCO2. Additional studies are needed to understand the species- and nutrientspecific nature of these responses and the underlying mechanisms involved. Recent results suggest that both carrier proteins and assimilatory enzymes associated with N uptake can be influenced by climate change.73 The activity of the putative NO −3 carrier, NRT1 in tomato roots was reduced at eCO2 at 37°C, but not at 30°C and eCO2. By comparison, the activity of AMT1 thought to be involved in NH +4 uptake was not altered by CO2 or temperature in this study. Activities of enzymes involved in N assimilation in roots including NO −3 reductase and GS-GOGAT were also reduced at eCO2 and 37°C. Thus, it appears that the function of enzymes/proteins involved in both uptake and

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assimilation of N can be expected to be altered by climate change. While the eCO2 effects were greatest, interactions with other environmental variables, such as drought and soil temperature, occur indicating that predicting N uptake in a changing climate may differ spatially depending on site-specific edaphic conditions.73 The interaction of enzyme function with climate change illustrates the daunting challenge of using targeted molecular approaches to enhance climate change adaptation of crops. Other plant root factors responsive to eCO2 can also impact nutrient uptake and assimilation. For example, Jin et al.85 reported that the quantity and composition of root exudates are likely to change due to altered root carbon fluxes and changes in root metabolism that accompany climate change. These root exudates may enhance P uptake by chelating sparingly soluble P, and/or by altering the biogeochemical cycling of P and microbial activity in the rhizosphere. However, most of these belowground interactions among roots and soil microorganisms in the context of climate change remain poorly understood, but could be critically important to our understanding of strategies to improve nutrient uptake. Of particular note is the dearth of knowledge regarding the importance of mycorrhizae in general NUE. These fungi form a symbiotic relationship with their host plants and greatly extend root surface in a manner similar to root hairs. Historically they have been assumed to be primarily relevant to P uptake in infertile environments. Although data are sparse, preliminary work indicate that extent of mycorrhizal symbiosis and plant N acquisition are enhanced in host plants grown in eCO2.86–88 Additional work is needed to understand the full potential of this symbiosis in enhancing uptake of macronutrients by a broader spectrum of plants in a changing climate including shifts in soil temperature and moisture.

RESEARCH PRIORITIES AND CLIMATE SMART AGRICULTURE “Climate smart” has emerged as the general term for the agricultural strategy that simultaneously accomplishes three goals: (1) sustainably increases agricultural productivity and income especially of smallholder farmers in the development context, (2) increases adaptation with a particular focus on the challenges imposed by a changing climate, and (3) reduces greenhouse gas emissions from agriculture when compared to business as usual.89 Climate smart agriculture (CSA) pursues implementation of flexible and local or context-specific solutions, and it is widely acknowledged that the existing knowledge-base is inadequate to support effective decision-making. The limitations associated with climate models including the uncertainties and down-scaling issues discussed above are typically highlighted as critical knowledge gaps for CSA.90 At present, fertilizer management strategies for NUE optimization are generally focused on making the right decision regarding four aspects of management [aka 4R Nutrient Management (http://www.nutrientstewardship.com/)]: using the right (1) rate of fertilizer; (2) source of fertilizer particularly important for N, (3) timing of application to maximize plant-availability and minimize edge-of-field losses, and (4) placement in the soil to maximize availability in zones of greatest uptake. Certainly, a better understanding of climate change impacts on soil moisture and temperature regimes for local contexts are prerequisite for successful implementation of 4R strategies. Indeed, many potential CSA-NUE strategies to reduce edge-of-field losses and enhance proximity between labile soil nutrients and active roots have been researched and proven for specific contexts (e.g., conservation tillage, microdosing near a seed, etc.). For example, mitigation options to reduce P edge-of-field losses and increase uptake efficiency and measures of agronomic NUE are well-reviewed91 and their efficacy hypothesized for a changing climate.17 Likewise, a host of N fertilizer products and additives (e.g., transformation inhibitors, fertilizer coatings, etc.) exist, have been demonstrated to enhance NUE under certain conditions and are advocated for within a CSA strategy.92

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The challenge is selecting the right NUE tool(s) for a context beyond the inference space of the empirical experimental conditions. The call by Lipper et al.90 for “robust studies that improve understanding of what works where and why in different agro-ecologies and farming systems…” is presumed to be generally inclusive of NUE but we highlight the following three priorities for the climate change/NUE research agenda: 1. Advance scientific understanding of root form and function: because of the difficulty in studying roots and the scant data available to support conclusions, numerous opportunities exist for advancing our understanding of the impact of climate change on root form and function. It is clear from studies to date that the environmental factors associated with climate change that impact nutrient uptake (temperature, drought, and eCO2) are not additive, but interact. Thus there is a desperate need for additional research exploring all climate-change drivers simultaneously using relevant levels of change based on IPCC model predictions.72 Although challenging and costly, integration of form and functional traits (e.g., root morphology including general architecture, root hairs and specialized structures, and nutrient uptake rates by roots) into the same analysis, instead of looking at a single trait or two, would advance our understanding of climate change impacts.78 Few studies also include management (e.g., fertilizer rate, form, placement) and plant genotype as factors in research aimed at understanding climate change impacts on nutrient uptake even though it is well-known that management x genotype x environment interactions are common in crop production.53 Finally, mycorrhizal contributions to plant NUE are woefully understudied and rhizosphere biology must be prioritized in all cropping systems research. 2. Improve representation of macronutrients in all major crop models: the information gleaned from existing literature and new initiatives in root system research must be assimilated into crop and agricultural systems models. Of the seven major global gridded crop models used by the Agricultural Model Intercomparison and Improvement Project93 in a recent assessment of agricultural risks of climate change,5 only three considered N and P stress and only one presented N, P, and K stress; other macronutrients were not considered. Further, the extent of model calibration was explicitly highlighted as a limitation as was robust handling of fertilizer applications. By inference, such observations highlight the magnitude of the knowledge gap that will impede implementation of effective NUE strategies for future climates. Model algorithms must be created for all macronutrients, and all nutrient algorithms need to be rigorously calibrated and verified for major crops, agroecozones and representative nutrient management strategies. 3. Update and/or develop plant and crop NUE diagnostics: as summarized above, research todate suggests that concentrations of critically important nutrients (e.g., N and K) in “nutrientsufficient” plants may be lower under future conditions of eCO2. In addition, it is important to note that widely used macronutrient critical values given in current texts typically source to references published in the 1980s, which, in turn cite research conducted in or before the 1970s (e.g., Havlin et al.27). While recent reports on modern hybrids confirm historic observations that macronutrient accumulation generally scales with plant biomass,94 realizing improved NUE in locally adapted crops will require a much more robust and nuanced framework against which to evaluate real-time observations of plant nutrient status. Although current interest is focused on tools for rapid, indirect sensing of large numbers of tissue samples, the interpretation of any such assessments still depends on the quality of the underpinning reference measures used to calibrate these indirect methods. At present, science is investing in several major initiatives to

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improve the quality and quantity of soils information for agriculture and management purposes [e.g., GlobalSoilMap.net (http://www.globalsoilmap.net/); Africa Soil Information Service (http:// africasoils.net/)]. We suggest that these large investments will be significantly leveraged with a concomitant investment in cost-effective diagnostics for various aspects of plant and crop NUE and are consistent with renewed interest globally in plant phenotyping.

CONCLUSIONS Climate change, and especially, eCO2 and altered temperature and moisture regimes are likely to impact NUE of plants. These changes will be manifested several ways including the influence of climate change on: (1) soil biology, physics, and chemistry that collectively alter macronutrient availability and mobility in the root zone and (2) the form and function of plant roots and shoots that have critical roles in nutrient uptake and sink strength including partitioning into economically important plant tissues. The uncertainties associated with predicting future soil temperature and moisture regimes are large, especially at regional/local scales where mitigation/adaptation strategies are likely to be deployed. In addition, the dearth of reports on eCO2 impacts on root biology and NUE make it difficult to predict responses of these systems/processes to climate variation. Deploying “climate smart” agriculture successfully depends on improved understanding of root form and function, improved representation of macronutrients in all major crop models, and updated or new diagnostics that are cost-effective and useful within the context of NUE phenotyping.

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Index A Abiotic stresses, 128, 165, 169, 189, 245, 252, 259 ACC (1-aminocyclopropane1-carboxylate) deaminase (ACCD) activity, 291 Acid phosphatases (APases), 323 5′-Adenylylsulfate reductase (APR), 220 Adsorption, 323, 361 Aegilops tauschii, 310 AEQ. See Aequorin (AEQ) Aequorin (AEQ), 188 Aerenchyma, 89 Agave tequilana, 297 inoculation, 296 Agronomic biofortification, 207 for micronutrients, 207 Agronomic management, 237 Agrostis capillaris, 367, 369 AlaAT. See Alanine aminotransferase (AlaAT) Alanine aminotransferase (AlaAT), 34, 110, 287 NUE technology, 110 Alternanthera philoxeroides, 20 Alternative oxidase (AOX), 258 Aluminum (Al), 323 Al resistance transcription factor 1 (ART1), 203 Al stress, 326 Aluminum-activated malate transporter (ALMT), 50 Amino acid permease 1 (AAP1), 288 1-Aminocyclopropane-1-carboxylic acid synthase (ACS) family, 204 Ammonium transporter AMT1, 105, 287 proteins, 287 Amplified fragment length polymorphism (AFLP) markers, 271 Annexins, 187 Antheraxanthin, 255 Anthesis, 238 Anthocyanin, 215 Antioxidants enzymatic, 248, 250 nonenzymatic, 248, 250 Antiquitin genes, 110 btg26, 110 OsAnt1, 110 APase activity, 325 APS4 transcript, 226 AQPs. See Plant aquaporins (AQPs) Arabidopsis (Arabidopsis thaliana), 86, 96, 149, 156, 236, 312, 324 Arabidopsis vacuolar pyrophosphatase1 (AVP1), 111  

AtAKT1 role in K+ acquisition, 347 endoplasmic reticulum-localized peroxidase, 152 rare cold induced gene 3 (AtRCI3), 152 ER-based low-phosphate root 1 (LPR1), 50 lateral root growth, 48 model for signaling pathway for P deficiency, 331 mutation of the gene CSLA9, 291 MYB-like transcription factor CAPRICE (AtCPC1), 155 nitrate transporter, 50 Opt4 (At5g64410) gene, 225 overexpressing AtNLP7 in, 343 phosphate deficiency response 2 (PDR2) proteins, 50 Pi-deficiency in, 46 protein phosphatase 2C (AtPP2C), 152 root foraging in, 54 SDI1 (At5g48850) gene, 224 SPX domain proteins (SPX), 50 SULTR1;2 gene promoter, 220 S use efficiency, 225 transcription factor (TF) PHR1, 52, 54 wak2 mutant, 128 Arabidopsis transcription factors, 155 AtMYB77, 155 knotted-like Arabidopsis transcription factor 6 (AtKNAT6), 155 MADS box transcription factor AtANR1, 155 no apical meristem cup-shaped cotyledon1 (AtNAC1), 155 Arabinogalactan gene rcph2, 291 Arabinogalactan proteins (AGPs) rich rhizodeposits in the soil, 291 Ascorbate (AsA), 248 Asparagine synthase, 34 Aspartate aminotransferase, 34 AtCIPK23-AtCBL1/9 complex, 152 ATG8, 111 AtMGT genes, 202 ATP-binding cassette subfamily B (ABCB) transporters, 125 Australian wheat cropping zone, 94 AuTophaGy-related gene, 111 Auxin, 32, 222, 291 Auxin permease1 (AUX1)/LAX influx carriers, 125 Auxin signaling auxin-signaling gene F-box3, 287 key regulators, 125 aux/indole acetic acid (Aux/IAA) proteins, 125 auxin response factor (ARF), 125 F-box protein, 125 transport inhibitor response 1 (TIR1), 125

381

382

Index

B Bacillus safensis, 291 castor/pollux, enhances expression of, 293 maize roots, upregulates genes, 87 Bacillus subtilis, 325 Beta vulgaris L, 2, 324 BIG gene, 222 Biological nitrogen fixation (BNF), 309 water availability, effect on, 234–235 Biotic stress, 249 Biparental crossing, 271 Brachypodium distachyon, 16 Brassica napus, 138, 277 Brassica napus oleifera, 15 Brassica oleracea, 156, 277 Brassica rapa, 16 Breeding crops, with improved nutrient use efficiency, 339 BT gene, 110 Buthionine sulfoximine, 221 bZIP transcription factor, 221

C Ca2+ ATPases, 183 types, 183 P-type ATPase II A family, 184 P-type ATPase II B family, 184 CAB2 gene, 198 cADPR-gated channels, 184 Ca2+-efflux transporters, 171 Calcifuges, 182 Calcineurin B-like proteins (CBLs), 172 interacting protein kinase 23 (AtCIPK23), 50, 149 Calcioles, 182 Calcium (Ca), 1, 45, 48, 323 as amendment in acid soils, 173 apoplastic flux, 167 binding proteins, 185–187 Ca-dependent protein kinases (CDPK), 186 calcinurin B-like proteins (CBLs), 187 calmodulin (CaM), 185 CCaMKs, 186 characters, 185 sensors, 185 Ca sequestering protein, 187 cell wall integrity, role in, 165 deficiency in plants, 166 cell wall development, effect on, 166 lower shoot-root ratio, 166 necrosis, role in, 166 phloem-immobile cation, 166 weathering and leaching, role of, 166 entry into leaf cells, 167

entry into root, 167 local and systemic signaling, limitations, 55 macronutrient deficiency, role in, 188–192 magnesium, 191–192 nitrate, 191 potassium, 188–191 protein modification, effect on, 190–191 transcriptional regulation, effect on, 189 membrane structure and function, role in, 165 membrane transporters, 183–184 memory, 184–185 plant nutrition, role in, 166 in plants, 182 Ca pectates formation, role in, 182 as cementing material, 182 role and requirement, 183 removal from cytosol, 183 Rhizobium growth and nodulation capacity, effect on, 174 sensing of limitations, 51 sensor proteins, 171–173 calcineurin B-like proteins (CBLs), 172 calcium-dependent protein kinases (CDPKs), 172–173 plant stress response, role in, 171–173 as signal, 168–173 calcium influx and signaling, channels involved, 168–169 cyclic nucleotide-gated channels, 169 glutamate-like receptors, 169 calcium influx and signaling, transporters involved, 170–171 autoinhibited Ca2+-ATPase (ACA) proteins, 171 cation/H+ exchangers (CAXs), 170 signal transduction pathway, role in, 181 signatures, 184–185 symplastic pathway, 167 transportation transpiration rate, effect of, 167 uptake and distribution, 167–168 by roots and delivery to xylem, 167 transport to shoot, 167–168 Calcium channels depolarization-activated cation channels (DACC), 168, 184 hyperpolarization-activated cation channels (HACC), 168, 184 voltage-independent cation channels (VICC), 168 Calcium-dependent protein kinases (CDPKs), 171 isolation from plants, 187 Calcium ion (Ca2+)-mediated signaling, 181 Calcium use efficiency (CaUE), 173–174 definition, 173 external factors, role of, 173 improvement of, 173 nutrients interaction, effect of, 174 Calmodulin-like proteins (CMLs), 171–172

Index

Calmodulins(CaMs), 171–172 binding protein kinases, 171 binding protein phosphatases, 171 Calreticulin, 187 CaMV35S promoter, 327 Canola oil, 215 Ca2+-P-type ATPases, 168 Carotenoids, 249 Ca2+ sensor complexes, 152 Catabolic pathways, 217 Cation/H+ exchangers (CHXs), 151 cbl2cbl3 mutant, 199 CBL-interacting kinase CIPK23, 150 CBL-interacting protein kinases (CIPK), 187, 205 Cellular K+ homeostasis, 348 Cellulose synthases, 291 C4 grasses, 234 Chelating agents, 325 Chloride channel, 287 Cisgenesis, 111 Citrate, 325 and Al tolerance, influenced by, 326 overproduction on Al tolerance, 326 Citrus sinensis, 206 CKX expression, 347 Climate change, 357 availability of macronutrients of mineral origin, 361–363 hypothetical relationship, nutrient concentration and yield in changing climate, 371 impacts on soil organic matter, and biogeochemistry, 360–361 influence, on availability of nutrients in soil, 358 emission of nitrous oxide, 360 measurement, of nutrient use efficiency (NUE) and potential impacts, 359 potential, for reduced NUE, increased edge-of-field losses, 359 soil organic matter (SOM), 358 nutrient uptake and physiological NUE, 363–364 research and climate smart agriculture, 373 root form and function, 371–373 shoot and root growth, 363–364 shoot form and function, 364–369 summary of NUE-relevant IPCC, 358 greenhouse gases, 358 Climate smart agriculture (CSA), 373 Cluster roots, 323 formation LaScr1, role of, 126 LaScr2, role of, 126 Cohesion-tension mechanism, 235 Constitutive triple response 1(ctr1), 127 Coronate-insensitive 1 (COI1), 153 CRISPR/Cas9 system, 105, 111

383

Crop breeding, 156, 233, 308 Crop improvement programs, 233 Crop nutrient efficiency, 136 Crop nutrition, metabolic and environmental control, 69 Crop physiology, 234 water and N interaction, impact on, 234 Crop plant life cycle, remobilization of nutrients in, 35 Crop plant nutrition, 216 Crop production, 307, 308 Crop yield, 66 atmospheric levels of CO2, 76 balanced fertilization with macronutrients, 66 law of the optimum, 67 nutrient deficiency effects, 75 synergistic effects of nutrients on, 72 Crossbreeding, 308 Crown roots, 86 CS-overexpressed plants, 326 C-terminally encoded peptides (CEPs), 15 Cucumis sativus, 138, 236 Cyclic nucleotide-gated channel (CNGC), 151, 168, 200 subunits, 169 calmodulin (CaM)-binding domains (CaMBD), 169 cyclic nucleotide monophosphate-binding (CNBD), 169 pore-forming loop, 169 transmembrane-spanning domains, 169 Cys-2/His-2 Zinc finger transcription factor, 127 Cys synthesis activity, 223 Cytokinin oxidase/dehydrogenase (CKX), 347 Cytokinin response 1 (CRE1) gene, 127 Cytokinins, 291 Cytosolic Ca wave, 184 Cytosolic K+ concentration, 339

D Deep rooting gene, DRO1, 136 Dehydroascorbate reductase (DHAR), 250 Dendrobrium houshanense, 300 Diacylglycerol (DAG), 187 Diazotrophic bacterial endophytes, 294 Dicarboxylates, 326 Dicranopteris dichotoma, 300 Digital flatbed scanner, 136 Dionaea muscipula, 150 Diversity arrays technology (DArT) assays, 272 DNA binding with one finger1 (DOF1) transcription factor, 110 DNA fingerprinting, 273, 307 DNA markers, 274, 275 application to plant breeding, 276 sequence-tagged sites (STS) markers, 274 SSR markers, 274 DNA methylation, 221, 226

384

Index

DNA polymerase enzyme, 191 DRO1 functions, 340 3D RSA reconstruction, 137 plastic mesh system, role of, 137, 139 Dwarfing controlling genes, 103 Rht-12, 103 Rht-B1, 103

E Early nodulation gene, 110 Efficiency of nutrient acquisition (ENA), 7 Efficiency of nutrient utilization (ENU), 7 Electron carriers, 247 Electron-transport chain (ETC), 245, 254 Endophytic bacteria contribute to nutrient acquisition, 294–297 nitrogen acquisition, 294–297 phosphate acquisition, 297 Endophytic microbes, 289 and nitrogen use efficiency in plants, 289 plant growth promotion benefits, 290 Environmental degradation, 216 Environment Protection Agency (EPA), 111 EPA. See Environment Protection Agency (EPA) Escherichia coli, 105 Essential mineral element, 215 Ethylene overproducer 1(eto1) gene, 127 N-Ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins, 149 Eucalyptus grandis, 369 European Food Safety Authority (EFSA), 111 Eutrophication, 40, 65 Evapotranspiration, 235

F Fe deficiency, 326 Fenton reaction, 252 Fertilizers, 216, 285 depletion of ozone layer, 5 efficiency, 287 impact, on environment, 5 N-fertilizers, and Haber-Bosch process, 5 Pi-fertilizers, 5 total world consumption of, 5 FeS clusters, 216 Fibrous root system, 86 Fossil fuel combustion, 215 Foyer-Halliwell-Asada cycle, 251, 254

G Gamma-glutamyl cylcotransferase, 225 Gamma-glutamylcysteine synthetase, 225

Gel-based root growth system, 138 Gene expression quantitative trait loci (eQTL) mapping, 314 Gene modification, 327 Gene pyramiding approach, 110 Genetically modified (GM) crops, 105 Genetic biofortification, 207 Genetic improvement, 339 Genetic manipulation, 287 sulfate assimilation genes, 226 and S use efficiency, 225 Genetic markers, 271 biochemical markers, 271 molecular markers, 271 morphological markers, 271 Genome mapping, 271 Genome-wide association study (GWAS), 7, 274 experiments in plants, advantages, 308–314 for macronutrient use efficiency, 308–310 of nitrogen use efficiency, 308–309 of phosphorus-deficiency-tolerance traits, 309–310 Genomic, 206 Genotyping by sequencing (GBS), 272, 312 Germplasm, 287 GhCKX expression in cotton, 347 GhmMDH1 gene, cotton (Gossypium hirsutum L.), 326 Gibberellins, 291 Ginkgo biloba, 300 Global cereal production, 31 Glucosinolates, 222, 223 homeostasis, 223, 224 Glutamate dehydrogenase (GDH), 105 Glutamate-like receptors domains, 169 physiological role, 169 carbon metabolism, 169 gravitropism, 169 photosynthesis, 169 phototropism, 169 root architecture, 169 stomatal movements, 169 Glutamate receptors (GLRs), 151 Glutamate synthase (GOGAT), 103 Glutamine-2-oxoglutarate aminotransferase (GOGAT) cycle, 34 Glutamine synthetase (GS), 34, 103, 105, 287, 342 Glutaredoxins, 251 Glutathione, 216, 225 depletion, 221 synthetase, 225 Glycine max, 8, 138, 235, 325 regulation of phosphate uptake, 8 Gossypium hirsutum, 2 GPC. See Grain protein concentration (GPC)

Index

Grain filling, 94 Grain protein concentration (GPC), 103, 345 Grain protein content locus (Gpc-B1), 95 Green revolution, 1, 65 Growth hormones, 222 GS. See Glutamine synthetase (GS) GS-GOGAT pathway activity, 105 GS isoenzyme (GS2), 346

H Haber-Weiss reaction, 252 H+ antiporters, 151 Harvest index (HI), 339 H+-ATPase, 188 Heavy metals, 225 High-affinity K+ transporters (HKTs), 151 High-affinity nitrate transporter, 105 High-affinity Pi transporters, 327 High-affinity sulfate transporters, 217 High-affinity transport systems (HATS), 236 High nitrogen insensitive9 (HNI9), 15 High-throughput phenotyping, 104 High throughput root phenotyping, 136 H+:K+ symporters, 150 Arabidopsis K+ uptake permease1 (AtKUP1), 151 KT/KUP/HAK transporters, 150 Hordeum vulgare, 2, 15, 172, 339 HORvu;Pht1;1 overexpression, 328 Hydrogen peroxide (H2O2), 247–248, 294 decomposition, 250 generation from O2, 247 Hydroponics-based system, 138 Hydroxyl radical (OH•), 248 hy5 mutant, 221 Hypoxia, 34

I Indole-3-acetic acid (IAA), 291 production of, 291 Inorganic P (Pi), 325 in concentration in soil solution, 4, 325 control of promoter of pht1;2, 325 exudation of organic acid, 323 increase of Pi uptake rate by expression of high-affinity Pi transporters, 327–328 increase of Pi uptake rate by root architecture modification, 329 in rhizosphere, 323 transporters located in root surfaces, 327 Inositol-1,4,5-triphosphate gated channels, 184 Intergovernmental Panel on Climate Change 5th Assessment Report (IPCC AR5), 357

385

Intrinsic proteins, 236 IPT gene, 347 transgenic plants with autoregulated expression of, 346 Iron (Fe), 323 Isocitrate dehydrogenase (ICDH), 326 Isopentenyl transferase (IPT) gene, 346 isp6+ in yeast, 340

J Jasmonic acid biosynthesis, 153

K K+ channels, 339 K+ efflux antiporters (KEAs), 151 α-Ketobutyrate, 291 K+ influx in roots, 339 K+/Na+ concentration ratio, 348 Kompetitive allele-specific PCR (KASPar), 272 K+ outward-rectifying channels (KORCs), 168 KT/KUP/HAK transporter gene family, 150 K use efficiency (KUE), 339 genetic manipulation of K transporters for improvement of, 348–349 K channels modification for higher K uptake and use, 347–348

L LASAP2 in tobacco, overexpression, 324 Leaf tip necrosis1 (LTN1) gene, 127 LeEXT1.1 gene, 325 Liming, 173 industrial wastes, use of, 173 Linkage disequilibrium (LD), 311 around mutation, 313 Lipoxygenase, 248 Lolium perenne, 15 Low-affinity transport systems (LATS), 236 Low-N fertilizer application, 95 Low phosphate root1 (LPR1) gene, 50, 126 Low-P soils, miRNAs, role of, 129 Lupinus albus, 12

M Macro-NUE, 6. See also Nitrogen use efficiency (NUE) efficiencies of acquisition, 7 impacts, on essential nutrients, 7 physiological and developmental processes, in plant, 6 utilization of nutrient, 7 Macronutrient deficiencies, 245, 259 Macronutrient use efficiency (MUE), 307, 315 gene variants identification using association test, 310

386

Index

MAGIC. See Multiparent advanced generation intercross (MAGIC) Magnesium (Mg), 45, 48 for enzyme catalysis, 48 homeostasis, 200–201 imbalance, 202–203 by some ions, 202–203 by stress factors, 202 leaching, 202 rainfall, effect of, 202 temperature, effect of, 202 local and systemic signaling, limitations, 55 morphogenesis remodeling mechanisms, 198–199 deficiency, 198–199 toxicity, 199 storage compartment for, 48 stresses genomic perspectives, 206–207 signaling of, 204–206 Mg deficiency, 204–205 Mg toxicity, 205–206 transporters, 200–201 use efficiency, strategies for, 207–208 agronomic biofortification, 207 genetic biofortification, 207 Magnesium ammonium phosphate (struvite), 207 Magnesium deficiency plant processes affected, 192 Mahler reaction, 247 Maize breeding, 96 Maize QTLs. See also Quantitative trait loci (QTLs) agronomic traits, association with, 133 root morphology, association with, 133 Malate, 325 Malate dehydrogenase (MDH) overexpression, 326 Penicillium oxalicum overexpressed tobacco, 326 Malondialdehyde (MDA), 252, 257 Mapping approaches, association mapping, 104 Mapping-by-sequence for gene identification of mutants in rice, 315–318 MutMap, 315, 316 MutMap+, 317 MutMap-GAP, 317 Marker-assisted selection (MAS), 271, 310 Matricaria chamomilla, 256 Mean residence time (MRT), 74 Mechanosensitive Ca2+ channels (MCC), 184 Medicago sativa, 15 Medicago truncatula, 8 Metabolic interactions, 72 Metal ions, 325 Mg chelatase, 206

Mg chelatase (MgCH), 199 Mg2+/H+ exchanger, 200 MgO as slow-release fertilizer, 207 MgSO4 foliar applications, 207 Mg2+ transporter genes, 203 Mineral nutrients, 2 Mineral nutrition, 69, 83 miR169defg isoform, 131 miRNA395 overexpression, 226 Mitochondrially localized SAT (SERAT2;2), 223 Mitogen-activated protein kinases (MAPK), 128, 173 Modern root measurement greenhouse-based growth methodologies, 136 laboratory growth methods, 136 aeroponic arrangement, 136 agar plates, 136 box and cylinder growth systems, 136 gel plates, 136 hydroponics, 136 paper/cloth pouches, 136 minirhizotron growth methods, 136 rhizotron growth methods, 136 Molecular-assisted breeding, 271–276 materials, marker-assisted evaluation, 274 methods, based on marker-assisted selection, 275 genomic-wide selection, 276 marker-assisted backcrossing, 275 marker-assisted gene pyramiding, 276 marker-assisted recurrent selection, 276 molecular markers, 271–273 QTL mapping and validation, 274 Molecular marker-assisted breeding (MAB), 267, 275 Molecular markers, 271–273 applications, 307 development for marker-assisted breeding, 274 first-generation, 271 fourth-generation, 272 second-generation, 272 third-generation, 272 Molecular platforms, 104 Monocarboxylates, 326 Monocot root system, 124 Monodehydroascorbate reductase (MDHAR), 250 msa1 mutant, 221 Multidrug resistance protein (MRP3), 198 Multiparent advanced generation intercross (MAGIC), 104 populations, 311 MutMap, 307 MYB-family of transcription factors, 224 MYB transcription factor (TF)-regulating Pi homeostasis, 328 Mycorrhiza, 33, 323

Index

N NAAT gene, 327 NAC transcription factor, 346 NAM. See Nested association mapping (NAM) NAR2 protein, 287 Nested association mapping (NAM), 104 population, 311 Next-generation sequencing (NGS), 273, 307, 312, 315 N+/H+ exchangers (NHXs), 152 Nicotianamine aminotransferase (NAAT), 327 Nicotiana plumbaginifolia, 105 Nicotinamide adenine dinucleotide phosphate (NADPH) oxidases, 152 NIN-Like Protein 7 (NLP7), 110 Nipponbare, 317 Nitrate assimilation, 105 Nitrate, principal N source, 287 Nitrate transporter 2 family (NRT2s), 341 Nitrate transporters, 287 Nitric oxide (NO), 287 Nitrogen (N), 1, 31, 45, 46, 84 absorption forms, content and functions in plants, 269 accumulation in grains, 339 application of nitrogenous fertilizers, 46 assimilation enzymes, 103, 233, 269 assimilation genes, 339 availability, 233, 235, 268 in cellular metabolism, 2 controlled network of transporters and sensors, 10 deficiency effect, 47 efficient maize hybrids, 344 efficient plants, 339 fertilizer, 233, 285 application, 345 forms, 233, 269 leaching, 238 limiting factors, for plant development, 2 local and systemic signaling, limitations, 53–54 metabolic enzymes, 236 molecular and genetic basis, efficiency of nitrate, 8 negative effects, of deficiency, 2 nitrate assimilation and mobilization, 17 N uptake in grains, 339 in plant growth and availability in soil, 3 sensing of limitations, 50 sources, 46 amino acids, 84 ammonium, 84 nitrate, 84 symbiotic root legume bacteria, 46 transporters, 236 Nitrogen assimilation efficiency (NAE), 286

387

Nitrogen cycling Mycorrhiza, role in, 235 water, role in, 234 Nitrogen fixation, 33 Arbuscular Mycorrhiza, role in, 235 biological, 234, 309 in legumes, during drought stress, 235 Rhizobium bacteria, 33 symbiotic, 234, 309 Nitrogen harvest index, 94 Nitrogen, phosphorous, and potassium (NPK), 285 acquisition, utilization genetic and molecular mechanisms involved in, 268–270 nitrogen, 269 phosphorus, 270 potassium, 270 efficient crop plants molecular marker-assisted strategies for development, 271 production using molecular-assisted breeding, 276–277 fertilizer, 267, 339 nutrients, 267 Nitrogen remobilization efficiency (NRE), 286 Nitrogen transfer evaluation, 297 mass spectrometry, 299 quantitative real-time PCR, 297–298 Nitrogen uptake efficiency (NUpE), 94, 286 physiological traits affecting, 287 Nitrogen use efficiency (NUE), 233, 245, 268, 285, 308 components uptake efficiency, 308 utilization efficiency, 308 definition, 93, 308 factors improving mesophyll conductance (gm), 234 oxidative stress reduction, 252 photosynthesis, 234 stomatal conductance (gs), 234 measurements, 96 in plants, 286 related genes, 95 strategies to improve, 94–95 by engineering root growth, 340–341 genetic approaches, 95–104 genetic information, use of, 104 genetic loci discovery, 96–104 genotypic variation identification, 96 by increasing postanthesis N uptake and delaying senescence, 344–347 by manipulation of transcription factors, 343–344 by N transporters manipulation, 341–343

388

Index

Nitrogen use efficiency (NUE) (cont.) transgenic approaches, 105–111 biotech approaches, 111 targeted approach, 105–111 uptake efficiency, increase in, 94 increasing uptake capacity, 94 root morphology, change in, 94 utilization efficiency, increase in, 94–95 delayed senescence (stay green), 95 photosynthetic N utilization efficiency (PsNUE), increase in, 94 remobilization efficiency, increase in, 95 traits associated biotech approaches, 106 genetic loci identification, 97 variation, 96 in water constrained environments, 237–238 agronomic practices, 237 genetic improvement of water and N-related traits, 237–238 root traits, 238 stay green, 238 Nitrogen utilization efficiency (NUtE), 286 p-Nitrophenyl phosphate, 324 Nitrous oxide biological denitrification and emission of, 360 No apical meristem (NAM) transcription factor, 95 Nodulation, 234 Nodule related gene, 110 Noncellulosic polysaccharides, 291 Nonenzymatic antioxidants, 248, 250 alkaloids, 248 ascorbate (AsA), 248 carotenoids, 248 flavonoids, 248 reduced glutathione (GSH), 248 tocopherol, 248 Nonphotochemical quenching (NPQ) of chlorophyll fluorescence, 254 Nonselective cation channels, 151 Nonspecific APase overexpressing plants, 324 Nonyellowing 1 (NYE1), 198 NO–3 reductase (NR), 103 NO–3 transporters, 94 NRT2.1, 105 NRT1.1B, 104 NRT2.3b, 105 NRT1/PTR family (NPFs), 341 N-signaling network, 343 NtPT1 overexpression, 327 N transport, 339 NUE-efficient genotypes, 287 NUpE. See Nitrogen uptake efficiency (NUpE)

Nutrient acquisition efficiency (NtAcE), 268 Nutrient assimilation, 217 Nutrient interactions crop production, 66 excess fertilization vs. optimal fertilization, 66 law of diminishing returns, 66 law of the minimum, 66, 67 law of the optimum, 66 relationship, biomass production and capture of nutrient, 68 single-factor limitation, 67 synergistic effects, 66 cation-anion balance, 69 immobilization and deficiency of nutrients, 70 soil salinity, 70 synergisms and antagonisms between nutrients, 69–71 Nutrient leaching, 267 Nutrient productivity (NtP), 268 Nutrient remobilization, 217 Nutrients, availability in soil, 4. See also Soil assimilation and remobilization, regulation, 16 central role of PHO1, in phosphate homeostasis, 16 nitrate, 17 potassium homeostasis, 18 components of, 6–7 development of plants, with acquisition and utilization efficiencies, 19 improvement, macronutrient use efficiency, 18–20 inorganic phosphate, 4 mechanisms, for uptake and transport, 8 in organic and inorganic chemical forms, 4 quantity and quality of, 4 replenishment, 5 use of fertilizers and reserves, 5 Nutrients, edge-of-field loss mechanisms for, 359 Nutrient signals, integration, 90 Nutrients, in soil, 358 influence of climate change on, 358 Nutrient-sufficient plants, 374 Nutrient turnover rates, 40 Nutrient uptake modeling, 94 Nutrient use efficiency (NtUE), 1, 31, 65, 173, 267, 285, 339, 357 ammonium impact, 32 assessment and evaluation of, 36 climate change impacts on, 357 comparison of indices and components, using original data, 39 crop production and related approaches of, 37–40 agronomic efficiency, 37 crop recovery efficiency, 37 fertilization treatments, 40 N accumulation, rate of, 38 nutrient conservation, for growth and quality development, 37

Index

nutrient fertilization, 37 physiological efficiency, 37 definition, 6, 40 ecological approaches of, 37 efficient recycling and allocation, to yield organ, 74 fertilizers/amendments application rate, effect of, 174 application time, effect of, 174 placement effect, 174 in future agriculture, 36 genes for, 35–36 genotypic variability, effect of, 173 impact of ambient (aCO) and elevated (eCO) atmospheric carbon dioxide concentrations, 366 impact of nitrogen fertilizer application, and atmospheric CO2, 368 improvement, in crops, 73 intraspecific genetic variability, effect of, 173 life cycle assessments (LCAs), 31 linear diffusion rates of K in soil, 364 metabolism and gene regulation, 33–35 ammonium, 34 biosynthesis of amino acids, 34 glutamine-2-oxoglutarate aminotransferase (GOGAT) cycle, 34 glutamine synthetase (GS), 34 transcriptional and enzyme levels, 34 nutrient balances/budgets/modeling/life cycle assessments, 40 nutrient interactions in plants, 68 availability of macronutrients, 68 balanced fertilization, 68 efficiency of agricultural production, 68 nutrient uptake efficiency vs. nutrient utilization efficiency, 73 N yield and, influenced by carbon dioxide level, 367 overview of approaches, for improvement and evaluation, 31 physiological aspects of, 364 physiology and genetics, 31 physiology and genetics of, 32 root development, nutrient availability, 32 root interactions with microorganisms, 33 potential, increased edge-of-field losses, 359–360 rare earths as fertilizers for improving, 299–301 relationship, phosphorus and potassium, 368 relevant IPCC climate change assessments, 358 remobilization efficiency, 74 remobilization of nutrients, in crop plant life cycle, 35 shoot and root growth, nutrient uptake and, 363 storage capacity, 74 trade-offs for improvement of, 65 utilization of atmospheric CO2, 76 Nutrient utilization efficiency (NtUtE), 268 Nutritional quality, 287

O OAS thiol lyase (OASTL), 223 Oilseed rape, 215 Omics profiling, 104 Organic acid, 323, 332 ALMT- and MATE-type transporters, 326 for Al tolerance, 326 APase for hydrolysis of phosphomonoester and solubilization of, 327 chelating ability with metallic ions, 325 exudation, 325 exudation from roots under low-P conditions, 327 transporters required to release, 326 Organic nitrogen (ON), 288 Organic phosphate (Po), 323 accumulation from ADP, 324 APase accessed to solubilized, 327 chemical property, 325 degradation of, 323 improve ability to mobilize, 324, 327 Orthophosphate, 324 Oryza sativa, 8 regulation of phosphate uptake, 8 Oryza sativa L., 234, 255 OsENOD93-1, 110 OsHAK5 overexpression, 348 OsMADS25 overexpression in transgenic rice, 341 OsNAP expression, 346 OsNAR2.1 gene, 341 OsPht1;4 overexpression, 327 OsPHY1 gene, 325 OsPT2-overexpressed soybean, 327 OSTL (OASTL-C), for S sensor mechanism, 223 OsTOND1 overexpressing, in rice, 341 Oxidative stress, 245, 251–253 causes, under NPK deficiency, 253–256 definition, 252 nitrogen deficiency and, 256–257 phosphorus deficiency and, 257 plant growth and development, effect on, 258 potassium deficiency and, 257 ROS as indicator, 253 thioredoxins, role in plant tolerance of, 256 variation, under N, P and K deficiency, 256–258 5-Oxoprolinase, 225 Oxygen detoxification, 206

P Peptide transporter PTR6, 105 Peroxiredoxins, 251 Phaseolus vugaris, 234 Phloem-specific proton-sucrose symporter, 198

389

390

Index

pho2 mutant, 130 Phosphatase genes, 324 Phosphate 2 gene, 127 Phosphate deficiency response2 (PDR2), 126 Phosphate deficiency response 2 (PDR2) protein, 50 Phosphate starvation response1 (PHR1), 126 Phosphate transporter1 gene, 8 Phosphate transporters (PHT), 127, 324 LaPT1, 128 phosphoenolpyruvate carboxylase 3 (LaPEPC3), 128 Phosphate transporter traffic facilitator 1 (PHF1), 270 Phosphoenolpyruvate carboxylase (PEPC), 34, 110, 326 Phosphogypsum mixture, 173 grain yield, effect on, 173 Phospholipase C (PLC), 187 Phosphomonoesterase (phosphatase), 324 Phosphorous, absorption forms, content and functions in plants, 269 Phosphorous acquisition efficiency (PAcE), 268 Phosphorous uptake efficiency (PUpE), 268 Phosphorous use efficiency (PUE), 268 Phosphorous utilization efficiency (PUtE), 268 Phosphorus (P), 1, 31, 45, 323 accumulation in TaALMT1 overexpressed plants, 326 accumulation of PvPAP3 overexpressed Arabidopsis, 324 acquisition efficiency genetic control of, 124 root system architecture (RSA), role of novel root system imaging methods, 136–138 acquisition mechanism, 123 miRNAs, role of, 128–132 root system architecture (RSA), molecular basis of, 124–128 application of fertilizers, 45 deficiency, 123, 323 QTL for root traits, 132–136 root-to-shoot dry weight ratio, 124 deficiency impact, 46 in dicot root, 46 efficiencies, of phosphate acquisition and utilization, 13 exudation in FRD3 line, 326 homeostasis, miR399, role of, 130 improvement of internal P use efficiency, 329 modification of carbon metabolisms, 329–330 increase of Pi uptake rate by expression of high-affinity Pi transporters, 327–328 limiting factors, for plant development, 2 local and systemic signaling, limitations, 52–53 mobilization and utilization, 46 molecular and genetic basis, efficiency of phosphate, 8 in monocots root, 46 negative effects, of deficiency, 2 noncoding RNAs roles in acclimation, 331

optimization of signaling networks involved in P stress responses, 330–331 phosphorylation of proteins and lipids, 2 Pi-deprivation responses and systemic signaling pathways, 47 in plant growth and availability in soil, 3 in plant metabolism, 46 regulation, of phosphate uptake, 8 arbuscular mycorrhizal (AM)-assisted pathways, 8 direct phosphate transporter (PT)-mediated uptake pathways, 8 master regulator (PHR1), 9 Pi efflux transporter PHO1 (Phosphate 1), 8 use of mycorrhizal fungi, 8 rhizosphere, 323 role of PHO1, in phosphate homeostasis, 16 sensing of limitations, 50 source of inorganic phosphate (Pi), 45 starvation genes, 125 HOX1, 125 PSTOL1, 125 starvation responses, 128 cluster root formation, 128 genes, expression of, 128 uptake efficiency, improvement, 323 uptake from Fe-P in soils, 326 Phosphorus deficiency tolerance index (PDTI), 310 Phosphorus-starvation tolerance 1 (PSTOL1), 125 receptor-like cytoplasmic kinase, 125 Photooxidative stress, 254 symptoms of, 255 Photoperiod controlling genes, 103 Ppd-A1, 103 Ppd-B1, 103 Photosensitizers, 247 Photosynthetic electron transport, 216 Photosynthetic nitrogen utilization efficiency (PsNUE), 94 Photosystem I (PS I), 247 Photosystem II (PS II), 247 PHR1 (Phosphate response 1), 9 in Arabidopsis thaliana, 9 MYB domain, 9 Pi-starvation, 9 SPXs proteins and, 9 phr1 mutant, 330 PHT1 overexpression, 327 Phytate-degrading activity, 325 Phytoalexins, 247 Phytohormones, 291 PIN-formed (PIN) efflux carriers, 125 Pinus radiata, 369 Pi signaling, 270 Pisum sativum, 15

Index

Pi transporters, high-affinity, 323 Plant aquaporins (AQPs), 236 Plant breeders, 310 Plant macronutrient, 45. See also entries as Nutrients calcium, 48 local and systemic signaling of, 51 magnesium, 48 nitrogen, 46–47 phosphorus, 45–46 potassium, 48 sulfur, 49 Plant-microbe interaction, 332 Plant-pathogen interactions, 247 Plant peroxisome, 247 Plasmodesma, 235 Plastoquinones, 247 Polymorphic markers, 273 Potassium (K), 1, 45, 48, 339 absorbtion by root hair cells, 188 absorption forms, content and functions in plants, 269 as catalysts, 2 deficiency plant sensing, schematic model of, 190 plant signaling, schematic model of, 190 homeostasis, 18 limiting factors, for plant development, 2 local and systemic signaling, limitations, 55 molecular and genetic basis, efficiency of, 8 negative effects, of deficiency, 2 network of transporters and channels, 10 auxins (AUXs) and NaCl-stress, 10 CIPK proteins, 11 protein-protein interaction, 11 Shaker and KUP/HAK/KT, 10 in plant growth and availability in soil, 3 sensing of limitations, 51 transporter protein families, 188 transporters, starvation-induced transcriptions of, 189 AtCHX13, 189 AtCHX17, 189 AtHAK5, 189 AtKEA5, 189 AtKUP3, 189 uptake channels, 188 Potassium (K+) ion deficiency signaling, 153 outwardly rectifying channels, 150 Arabidopsis guard cell outward rectifier (AtGORK), 150 regulatory components of, 152–153 deficiency signaling, 152–153 transport, 152 Ca2+, role of, 152

roles, 149 sensing, 149 sensor, 149 shaker-type inwardly rectifying channel, 149 Arabidopsis K+ transporter1 (AKT1), 149 barley HvAKT1, 149 grapevine VvK1.1, 149 rice OsAKT1, 149 tomato LKT1, 149 solubilizing microorganisms, 155 Bacillus coagulans, 155 Bacillus edaphicus, 155 Bacillus megaterium, 155 Bacillus mucilaginosus, 155 by improving uptake and translocation, 155–156 by increasing access, 155 by increasing availability, 155 transport mechanisms, 149–152 Potassium use efficiency (KUE), 268 Primary macronutrients, 83 Prosopis rhizobia, 234 Proteaceae, 323 Proteoid roots, 323 Protoporphyrin IX, 199 Pseudomonas aeruginosa overexpression of citrate synthase (CS), 326 PsNUE. See Photosynthetic nitrogen utilization efficiency (PsNUE) PSTOL1 gene, 89 PSTOL1 proteins, 136 pUbi promoter, 341 Purple acid phosphatase (PAP) family, 324 Pyruvate kinase (PK), 110

Q Quantitative trait loci (QTLs), 7, 96, 103, 104, 135, 267, 274 agronomic traits, 96 mapping, 35, 89, 96, 104, 274, 307 GWAS and, 311 meta-QTL analysis, 103 P utilization efficiency, 135 root-pulling resistance, role in, 135 root traits, effect on, 135 validation, 274

R Radical detoxification, 206 Random amplified polymorphic DNA (RAPD), 271 markers, advantages, 271 Rare earth elements (RE), 286 Rare earths, as fertilizers, 299

391

392

Index

Reactive oxygen species (ROS), 152, 189, 294 antioxidants interaction with, 248–252 detoxification, 251 hydrogen peroxide, 247–248 hydroxyl radical, 248 origins of, 245–248 plant signaling pathways, role in, 189 scavenging mechanisms, 248 signatures, 189 singlet oxygen, 247 superoxide anion, 247–248 toxicity of, 248–249 Regulatory interactions, 71 RE3+ ions, 299 Respiratory burst oxidase homolog C (AtRBOHC), 152 Restriction fragment length polymorphism (RFLPs), 271 advantages, 271 Rhizobium-legume symbiosis, 234 Rhizobium meliloti, 234 Rhizosphere, 324 mobilization of sparingly available P in, 328 Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), 94 photorespiratory losses, role in, 94 Robinia pseudoacacia, 234 Root biomass, 371 Root cortical aerenchyma, 89 Root foraging, nitrogen deficiency, 47 Root hair defective 2 (AtRHD2), 152 Root hair defective 6-like4 (RSL4), 126 Rootless concerning crown and seminal roots (RTCS), 126 Rootless with undetectable meristem 1(RUM1), 125 Root morphology, interactive effects, 72 RootReader 3D software, 137 Root-secreted organic acids, 325 Root-secreted phytase, trials using, 324–325 Root-shoot interrelationships, 357 Root surfaces, 323 Root system architecture (RSA), 1, 12, 32, 46, 75–76, 83, 123, 238, 268 analysis, response to soil nutrients, 85 cell division in primary root meristem, effect of, 124 changes in response to phosphorus miRNAs, role of, 131–132 enhancing phosphorus uptake via modulation miRNAs, role of, 129–131 genetic regulation of, 89–90 ideotypes, for nitrogen and phosphorus acquisition, 87 integration of nutrient signals, 90 macronutrient localization and mobility, 83 nitrogen, 84 phosphorus, 84 potassium, 84

miRNAs, role of, 128–132 miR399-triggered changes, 130 via OsPHR2, 130 morphology and anatomy, 88 nitrate availability, 14–15 nutritional regulation of, 14 plasticity, to phosphate availability, 12 efficiencies, of phosphate acquisition and utilization, 13 epigenetic modifications, 13 root hairs (RHs), 12 topsoil foraging, 12 potassium availability, 15–16 root morphology, 83 soil nutrients, 86 accessibility and acquisition of phosphorus, 87 ideotypes, for nitrogen and phosphorus acquisition, 88 resources, at expense of yield, 88 root proliferation, 87 size and distribution of the root system, 87 structural features of, 84 trade-offs with root-phenotyping approaches, 85 types, of root systems, 86 fibrous root system of monocot, 86 tap root system of dicots, 86 ROS. See Reactive oxygen species (ROS) RSA. See Root system architecture (RSA)

S SAG12 gene, 346 S-containing compounds, 226 S deficiency, 222 SDI transcriptional repressors, 224 Secale cereale, 15 Second messengers calcium ion (Ca2+) as, 181 cyclic GMP (cGMP) as, 181 inositol-3-phosphate (IP3) as, 181 Secretory acid phosphatases (APases), 323 Seminal roots, 86 Sequencing technology advances, 104 S fertilization, 216 Simple sequence repeats (SSR), 272 S-induced defense, 216 S-induced resistance, 216 Single-nucleotide polymorphisms (SNP), 272, 315 profiling, 104 Singlet oxygen (1O2), 247 origin of, 247 quenching of, 249 Soil degradation, 4 application of mineral fertilizers, 4 caused by, 4

Index

Soil-derived nutrients, macronutrients, 45 Soil management practices, 174 NUE improvement, role in, 174 nutrient cycling, role in, 174 Soil organic matter (SOM), 358 Soils availability of nutrients in, 4 based greenhouse trial, 132 calcium (Ca) content, 165 as cation, 165 as exchange complex, 165 as structural component, 165 climate change impacts on organic matter and biogeochemistry, 360–361 exploration, 32 fertility, 4 inorganic phosphate, concentration in, 4 mineral, 325 moisture content, 363 nutrient ion diffusivity in, 362 use of fertilizers and nutrient reserves, 5 Soil water availability, 233, 234, 237 nitrogen supply and, 235–237 nitrogen uptake and, 233 Solanum lycopersicum, 12, 236, 324 Solanum tuberosum, 325 Solubilization of phytate, 325 of sparingly soluble inorganic phosphate, 325 Sorghum bicolor, 345 Sorghum bicolor PSTOL1 (SbPSTOL1), 135 S overfertilization, 216 S oversupply, 216 Spermine, 249 Spinacia oleracea L., 234 Sprengel, Carl, 215 Squamosa promoter binding protein-like (SPL) genes, 131 S-responsive expression, 220 S runoff, 216 S-sensing/signaling mechanism, 223 S starvation, 218, 221, 224 Stabilization, 325 Starch biosynthesis genes, 199 ADP-glucose pyrophosphorylase large subunit 1 (APL1), 199 granule bound starch synthase 1 (GBSS1), 199 starch synthase 1 (SS1), 199 Starch degradation genes, 199 amylase 1 (AMY1), 199 β−amylase (BAM1), 199 Stelar K+ outward rectifier (AtSKOR), 150 Stem cells localization transcription factor, role of, 126

393

Sucrose accumulation, 48 Sugar beet, 324 Sulfate concentration, 217 Sulfate mobilization, 217 Sulfate transport, 217, 222–223 mediate efficient sulfate translocation, 219–220 Sulfur (S), 45, 49, 215 in agriculture, 215 deficiency on plant growth, 215 local and systemic signaling, limitations, 56 sensing of limitations, 51 sensor, 222–223 starvation response, regulation of, 220 Sulfur dioxide, 217 Sulfur limitation 1 (SLIM1) regulator, 56 Sulfur mobilization, 223 Sulfur-Responsive Element (SURE), 221 SULTR1/2 overexpression, 226 SULTR proteins, 219 SULTR1/2 transcription, 226 SULTR transporters, 219 Superoxide anion (O2•−), 247–248 Superoxide dismutase (SOD), 247 classification, 249 S usage efficiency, 217 Su(var) 3-9 homologs 6 (SUVH6), 131 Symbiotic nitrogen fixation (SNF), 234, 309 Systematic failures, 358 Systems biology, 217

T TaMADS51 overexpressed tobacco, 330 TaNAC2-5A-overexpressing transgenic wheat lines, 340 Tandem-pore K+ channels (TPKs), 151 Tap root system, 86 Targeting induced local lesions in genomes (TILLING), 104 T-DNA-knockout mutant of GGCT2;1, 225 Tetravalent states, 299 Thiamine thiazole synthase (THI1), 173 Thioredoxins, 251, 256 Tiling arrays, 104 TILLING. See Targeting induced local lesions in genomes (TILLING) Tissue nutrient concentrations, 369 Tocopherol, 249 antioxidant activity of, 249 types, 249 Tomato, 324 Tonoplast Ca2+ transporters, 167 storage, role in, 167 Tonoplast-localized calcium sensors, 205

394

Index

Tonoplast-located K transporters, 191 Topsoil P foraging, 136 Transcriptomic analysis, 206, 330 Translational repressors, 129 Transpiration, 233, 235, 237 Trifolium pratense, 15 Trifolium subterraneum, 325 Triticum aestivum, 8, 308, 339 regulation of phosphate uptake, 8 Triticum durum, 300 Triticum monoccum, 20 Triticum turgidum, 20

U Ubiquitin conjugating enzyme (UBC), 129 Ubiquitous nonenzymatic antioxidants, 249 Urea transporters, 288 DUR3, 105 Ureides, 34

V Vacuolar two-pore K+ (TPK) channel OsTPKb, 348 Vernalization controlling genes, 103 Vrn-A1, 103 Vrn-D1, 103

W Wall-associated kinases (WAKs), 128 Water movement pathways, from soil to root, 235 apoplastic pathway, 235 symplastic pathway, 235 transcellular pathway, 235 Water stress, 233–235, 237, 238 Water uptake, 235 root architecture, role in, 238 root hydraulic conductivity (Lpr), dependence, 235 Water use efficiency (WUE), 233 factors improving mesophyll conductance (gm), 234 photosynthesis, 234 stomatal conductance (gs), 234 Whole-genome sequencing (WGS), 312

X Xanthine oxidase, 247 Xenopus laevis, 203

Z Zea mays, 8, 235, 339 regulation of phosphate uptake, 8 Zeaxanthin, 255